Neural networks and fuzzy systems Books
£71.10
Springer Verlag, Singapore Hesitant Fuzzy Set: Theory and Extension
Book SynopsisCovering a wide range of notions concerning hesitant fuzzy set and its extensions, this book provides a comprehensive reference to the topic. In the case where different sources of vagueness appear simultaneously, the concept of fuzzy set is not able to properly model the uncertainty, imprecise and vague information. In order to overcome such a limitation, different types of fuzzy extension have been introduced so far. Among them, hesitant fuzzy set was first introduced in 2010, and the existing extensions of hesitant fuzzy set have been encountering an increasing interest and attracting more and more attentions up to now. It is not an exaggeration to say that the recent decade has seen the blossoming of a larger set of techniques and theoretical outcomes for hesitant fuzzy set together with its extensions as well as applications.As the research has moved beyond its infancy, and now it is entering a maturing phase with increased numbers and types of extensions, this book aims to give a comprehensive review of such researches. Presenting the review of many and important types of hesitant fuzzy extensions, and including references to a large number of related publications, this book will serve as a useful reference book for researchers in this field.Table of ContentsChapter 1: Hesitant Fuzzy Set.- Chapter 2: Hesitant Fuzzy Linguistic Term Set.- Chapter 3: Neutrosophic Hesitant Fuzzy Set.- Chapter 4: Pythagorean Hesitant Fuzzy Set.- Chapter 5: q-Rung Orthopair Hesitant Fuzzy Set.- Chapter 6: Probabilistic Hesitant Fuzzy Set.- Chapter 7: Type 2 Hesitant Fuzzy Set.- Chapter 8: Hesitant Bipolar Fuzzy Set.- Chapter 9: Cubic Hesitant Fuzzy Set.- Chapter 10: Complex Hesitant Fuzzy Set.- Chapter 11: Picture Hesitant Fuzzy Set.- Chapter 12: Spherical Hesitant Fuzzy Set.
£98.99
Springer Verlag, Singapore Convolutional Neural Networks for Medical Applications
Book SynopsisConvolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a research scholar to identify some futuristic problem areas. The topics covered include brain tumor classification, pneumonia image classification, white blood cell classification, skin cancer classification and diabetic retinopathy detection. The first chapter will begin by introducing various topics used in training CNNs to help readers with common concepts covered across the book. Each chapter begins by providing information about the disease, its implications to the affected and how the use of CNNs can help to tackle issues faced in healthcare. Readers would be exposed to various performance enhancement techniques, which have been tried and tested successfully, such as specific data augmentations and image processing techniques utilized to improve the accuracy of the models.Table of Contents
£37.99
Springer Verlag, Singapore Neural Information Processing: 29th International
Book SynopsisThe four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.Table of ContentsTheory and Algorithms.- Knowledge Transfer from Situation Evaluation to Multi-agent Reinforcement Learning.- Sequential three-way rules class-overlap under-sampling based on fuzzy hierarchical subspace for imbalanced data.- Two-stage Multilayer Perceptron Hawkes Process.- The Context Hierarchical Contrastive Learning for Time Series in Frequency Domain.- Hawkes Process via Graph Contrastive Discriminant representation Learning and Transformer capturing long-term dependencies.- A Temporal Consistency Enhancement Algorithm Based On Pixel Flicker Correction.- Data representation and clustering with double low-rank constraints.- RoMA: a Method for Neural Network Robustness Measurement and Assessment.- Independent Relationship Detection for Real-Time Scene Graph Generation.- A multi-label feature selection method based on feature graph with ridge regression and eigenvector centrality.- O3GPT: A Guidance-Oriented Periodic Testing Framework with Online Learning, Online Testing, and Online Feedback.- AFFSRN: Attention-Based Feature Fusion Super-Resolution Network.- Temporal-Sequential Learning with Columnar-Structured Spiking Neural Networks.- Graph Attention Transformer Network for Robust Visual Tracking.- GCL-KGE:Graph Contrastive Learning for Knowledge Graph Embedding.- Towards a Unified Benchmark for Reinforcement Learning in Sparse Reward Environments.- Effect of Logistic Activation Function and Multiplicative Input Noise on DNN-kWTA model.- A High-Speed SSVEP-Based Speller Using Continuous Spelling Method.- AAT: Non-Local Networks for Sim-to-Real Adversarial Augmentation Transfer.- Aggregating Intra-class and Inter-class information for Multi-label Text Classification.- Fast estimation of multidimensional regression functions by the Parzen kernel-based method.- ReGAE: Graph autoencoder based on recursive neural networks.- Efficient Uncertainty Quantification for Under-constraint Prediction following Learning using MCMC.- SMART: A Robustness Evaluation Framework for Neural Networks.- Time-aware Quaternion Convolutional Network for Temporal Knowledge Graph Reasoning.- SumBART - An improved BART model for abstractive text summarization.- Saliency-Guided Learned Image Compression for Object Detection.- Multi-Label Learning with Data Self-Augmentation.- MnRec: A News Recommendation Fusion Model Combining Multi-granularity Information.- Infinite Label Selection Method for Mutil-label Classification.- Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning.- Searching for Textual Adversarial Examples with Learned Strategy.- Multivariate Time Series Retrieval with Binary Coding from Transformer. -Learning TSP Combinatorial Search and Optimization with Heuristic Search.- A Joint Learning Model for Open Set Recognition with Post-processing.- Cross-Layer Fusion for Feature Distillation.- MCHPT: A Weakly Supervise Based Merchant Pre-trained Model.- Progressive Latent Replay for efficient Generative Rehearsal.- Generalization Bounds for Set-to-Set Matching with Negative Sampling.- ADA: An Attention-Based Data Augmentation Approach to Handle Imbalanced Textual Datasets.- Countering the Anti-detection Adversarial Attacks.- Evolving Temporal Knowledge Graphs by Iterative Spatio-Temporal Walks.- Improving Knowledge Graph Embedding Using Dynamic Aggregation of Neighbor Information.- Generative Generalized Zero-Shot Learning based on Auxiliary-Features.- Learning Stable Representations with Progressive Autoencoder (PAE).- Effect of Image Down-sampling on Detection of Adversarial Examples .- Boosting the Robustness of Neural Networks with M-PGD.- StatMix: Data augmentation method that relies on image statistics in federated learning.- Classification by Components Including Chow's Reject Option. -Community discovery algorithm based on improved deep sparse autoencoder.- Fairly Constricted Multi-Objective Particle Swarm Optimization.- Argument Classification with BERT plus Contextual, Structural and Syntactic Features as Text.- Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient.- Optimizing Knowledge Distillation Via Shallow Texture Knowledge Transfer.- Unsupervised Domain Adaptation Supplemented with Generated Images.- MAR2MIX: A Novel Model for Dynamic Problem in Multi-Agent Reinforcement Learning.- Adversarial Training with Knowledge Distillation Considering Intermediate Representations in CNNs.- Deep Contrastive Multi-view Subspace Clustering.
£85.49
Springer Verlag, Singapore Neural Information Processing: 30th International
Book SynopsisThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. Table of ContentsTheory and Algorithms.- Efficient Lightweight Network with Transformer-based Distillation for Micro-crack Detection of Solar Cells.- {MTLAN: Multi-Task Learning and Auxiliary Network for Enhanced Sentence Embedding.- Correlated Online k-Nearest Neighbors Regressor Chain for Online Multi-Output Regression.- Evolutionary Computation for Berth Allocation Problems: A Survey.- Cognitive Neurosciences.- Privacy-Preserving Travel Time Prediction for Internet of Vehicles: A Crowdsensing and Federated Learning Approach.- A Fine-Grained Domain Adaptation Method for Cross-Session Vigilance Estimation in SSVEP-Based BCI.- RMPE:Reducing Residual Membrane Potential Error for Enabling High-accuracy and Ultra-low-latency Spiking Neural Networks.- An improved target searching and imaging method for CSAR.- Block-Matching Multi-Pedestrian Tracking.- RPF3D: Range-Pillar Feature Deep Fusion 3D Detector for Autonomous Driving.- Traffic Signal Control Optimization Based on Deep Reinforcement Learning With Attention Mechanisms.- CMCI: A Robust Multimodal Fusion Method For Spiking Neural Networks.- A Weakly Supervised Deep Learning Model for Alzheimer's Disease Prognosis Using MRI and Incomplete Labels.- Two-Stream Spectral-Temporal Denoising Network for End-to-end Robust EEG-based Emotion Recognition.- Brain-inspired Binaural Sound Source Localization Method Based On Liquid State Machine.- A Causality-Based Interpretable Cognitive Diagnosis Model.- RoBrain: Towards Robust Brain-to-Image Reconstruction via Cross-Domain Contrastive Learning.- High-dimensional multi-objective PSO based on radial projection.- Link Prediction Based on the Sub-graphs Learning with Fused Features.- Naturalistic Emotion Recognition Using EEG and Eye Movements.- Task Scheduling With Improved Particle Swarm Optimization In Cloud Data Center.- Traffic Signal Optimization at T-shaped intersections Based on Deep Q Networks.- A Multi-task Framework for Solving Multimodal Multiobjective Optimization Problems.- Domain Generalized Object Detection with Triple Graph Reasoning Network.- RPUC: Semi-supervised 3D Biomedical Image Segmentation through Rectified Pyramid Unsupervised Consistency.- Cancellable iris recognition scheme based on inversion fusion and local ranking.- EWMIGCN: Emotional Weighting based Multimodal Interaction Graph Convolutional Networks for Personalized Prediction.- Neighborhood Learning for Artificial Bee Colony Algorithm: A Mini-survey.- Human Centred Computing.- Channel Attention Separable Convolution Network for Skin Lesion Segmentation.- A DNN-based Learning Framework for Continuous Movements Segmentation.- Neural-Symbolic Recommendation with Graph-Enhanced Information.- Contrastive Hierarchical Gating Networks for Rating Prediction.- Interactive Selection Recommendation Based on the Multi-Head Attention Graph Neural Network.- CM-TCN: Channel-aware Multi-scale Temporal Convolutional Networks For Speech Emotion Recognition.- FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependencies.- Domain-Invariant Task Optimization for Cross-domain Recommendation.- Ensemble of randomized neural network and boosted trees for eye tracking-based driver situation awareness recognition and interpretation.- Temporal Modeling Approach for Video Action Recognition Based on Vision-Language Models.- A Deep Learning Framework with Pruning RoI Proposal for Dental Caries Detection in Panoramic X-ray Images.- User stance aware network for rumor detection using semantic relation inference and temporal graph convolution.- IEEG-CT: A CNN and Transformer Based Method for Intracranial EEG Signal Classification.- Multi-Task Learning Network for Automatic Pancreatic Tumor Segmentation and Classification with Inter-Network Channel Feature Fusion.- Fast and Efficient Brain Extraction with Recursive MLP based 3D UNet.- A Hip-Knee Joint Coordination Evaluation System in Hemiplegic Individuals Based on Cyclogram Analysis.- Evaluation of football players' performance based on Multi-Criteria Decision Analysis approach and sensitivity analysis.
£75.99
Springer Verlag, Singapore Neural Information Processing: 30th International
Book SynopsisThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. Table of ContentsText to Image Generation with Conformer-GAN.- MGFNet: A Multi-Granularity Feature Fusion and Mining Network for Visible-Infrared Person Re-Identification.- Isomorphic Dual-Branch Network for Non-homogeneous Image Dehazing and Super-Resolution.- Hi-Stega : A Hierarchical Linguistic Steganography Framework Combining Retrieval and Generation.- Effi-Seg: Rethinking EfficientNet Architecture for Real-time Semantic Segmentation.- Quantum Autoencoder Frameworks for Network Anomaly Detection.- Spatially-Aware Human-Object Interaction Detection with Cross-Modal Enhancement.- Intelligent trajectory tracking control of unmanned parafoil system based on SAC optimized LADRC.- CATS: Connection-aware and Interaction-based Text Steganalysis in Social Networks.- Syntax Tree Constrained Graph Network for Visual Question Answering.- CKR-Calibrator: Convolution Kernel Robustness Evaluation and Calibration.- SGLP-Net: Sparse Graph Label Propagation Network for Weakly-Supervised Temporal Action Localization.- VFIQ: A Novel Model of ViT-FSIMc Hybrid Siamese Network for Image Quality Assessment.- Spiking Reinforcement Learning for Weakly-supervised Anomaly Detection.- Resource-aware DNN Partitioning for Privacy-sensitive Edge-Cloud Systems.- A frequency reconfigurable multi-mode printed antenna.- Multi-view Contrastive learning for Knowledge-aware Recommendation.- PYGC: a PinYin Language Model Guided Correction Model for Chinese Spell Checking.- Empirical Analysis of Multi-label Classification on GitterCom using BERT.- A lightweight safety helmet detection network based on bidirectional connection module and Polarized Self-Attention.- Direct Inter-Intra View Association for Light Field Super-Resolution.- Responsive CPG-Based Locomotion Control for Quadruped Robots.- Vessel Behavior Anomaly Detection using Graph Attention Network.- TASFormer: Task-aware Image Segmentation Transformer.- Unsupervised Joint-Semantics Autoencoder Hashing for Multimedia Retrieval.- TKGR-RHETNE：A New Temporal Knowledge Graph Reasoning Model via Jointly Modeling Relevant Historical Event and Temporal Neighborhood Event Context.- High-Resolution Self-Attention with Fair Loss for Point Cloud Segmentation.- Transformer-based Video Deinterlacing Method.- SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack.- CSEC: A Chinese Semantic Error Correction Dataset for Written Correction.- Contrastive Kernel Subspace Clustering.- UATR: An Uncertainty Aware Two-stage Refinement Model for Targeted Sentiment Analysis.- AttIN: Paying More Attention to Neighborhood Information for Entity Typing in Knowledge Graphs.- Text-based Person Re-ID by Saliency Mask and Dynamic Label Smoothing.- Robust Multi-view Spectral Clustering with Auto-encoder for Preserving Information.- Learnable Color Image Zero-Watermarking Based on Feature Comparison.- P-IoU: Accurate Motion Prediction based Data Association for Multi-Object Tracking.- WCA-VFnet:a dedicated complex forest smoke fire detector.- Label Selection Algorithm Based on Ant Colony Optimization and Reinforcement Learning for Multi-label Classification.- Reversible Data Hiding Based on Adaptive Embedding with Local Complexity.- Generalized Category Discovery with Clustering Assignment Consistency.- CInvISP: Conditional Invertible Image Signal Processing Pipeline.- Ignored Details in Eyes: Exposing GAN-generated Faces by Sclera.- A Developer Recommendation Method Based on Disentangled.- Graph Convolutional Network.- Novel Method for Radar Echo Target Detection.
£66.49
Elsevier Science Neural Networks Modeling and Control
Table of Contents1. Introduction2. Mathematical preliminaries3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays7. Concluding remarks and future trends AppendixA. Artificial neural networksB. Linear induction motor prototypeC. Differential robot prototype
£103.50
Taylor & Francis Ltd Deep Neural Network Applications
a huge range and FREE tracked UK delivery on ALL orders.
£142.50
Taylor & Francis Ltd Neural Network Perspectives on Cognition and Adaptive Robotics
a huge range and FREE tracked UK delivery on ALL orders.
£54.14
Taylor & Francis Ltd Deep Learning in Practice
a huge range and FREE tracked UK delivery on ALL orders.
£74.09
Taylor & Francis Ltd AI for Cars
Book SynopsisArtificial Intelligence (AI) is undoubtedly playing an increasingly significant role in automobile technology. In fact, cars inhabit one of just a few domains where you will find many AI innovations packed into a single product.AI for Cars provides a brief guided tour through many different AI landscapes including robotics, image and speech processing, recommender systems and onto deep learning, all within the automobile world. From pedestrian detection to driver monitoring to recommendation engines, the book discusses the background, research and progress thousands of talented engineers and researchers have achieved thus far, and their plans to deploy this life-saving technology all over the world.Table of ContentsForeword Preface AI for Advanced Driver Assistance Systems Automatic Parking Traffic Sign Recognition Driver Monitoring System Summary AI for Autonomous Driving Perception Planning Motion Control Summary AI for In-Vehicle Infotainment Systems Gesture Control Voice Assistant User Action Prediction Summary AI for Research & Development Automated Rules Generation Virtual Testing Platform Synthetic Scenario Generation Summary AI for Services Predictive Diagnostics Predictive Maintenance Driver Behavior Analysis Summary The Future of AI in Cars A Tale Of Two Paradigms AI & Car Safety AI & Car Security Summary Further Reading References
£22.99
Taylor & Francis Ltd AI for Cars
a huge range and FREE tracked UK delivery on ALL orders.
£114.00
Taylor & Francis Ltd Transfer Learning through Embedding Spaces
a huge range and FREE tracked UK delivery on ALL orders.
£42.74
Taylor & Francis Ltd Federated AI for RealWorld Business Scenarios
a huge range and FREE tracked UK delivery on ALL orders.
£137.75
Taylor & Francis Ltd Neural Networks for Applied Sciences and Engineering
a huge range and FREE tracked UK delivery on ALL orders.
£123.50
Taylor & Francis Ltd Neural Computing An Introduction
a huge range and FREE tracked UK delivery on ALL orders.
£56.99
CRC Press Current Applications of Deep Learning in Cancer Diagnostics
Book SynopsisThis book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.
£42.74
Taylor & Francis Ltd Cognitive and Neural Modelling for Visual
Book SynopsisFocusing on how visual information is represented, stored and extracted in the human brain, this book uses cognitive neural modeling in order to show how visual information is represented and memorized in the brain. Breaking through traditional visual information processing methods, the author combines our understanding of perception and memory from the human brain with computer vision technology, and provides a new approach for image recognition and classification. While biological visual cognition models and human brain memory models are established, applications such as pest recognition and carrot detection are also involved in this book.Given the range of topics covered, this book is a valuable resource for students, researchers and practitioners interested in the rapidly evolving field of neurocomputing, computer vision and machine learning.Table of Contents1. Introduction 2. Methods of visual perception and memory modeling 3. Bio-inspired model for object recognition based on histogram of oriented gradients 4. Modeling object recognition in visual cortex using multiple firing K-means and non-negative sparse coding 5. Biological modeling of human visual system using GLoP filters and sparse coding on multi-manifolds 6. Increment learning and rapid retrieval of visual information based on pattern association memory 7. Memory modeling based on free energy theory and restricted Boltzmann machine 8. Research on insect pest image detection and recognition based on bio-inspired methods 9. Carrot defect detection and grading based on computer vision and deep learning
£74.09
Taylor & Francis Ltd AI for Finance
Book SynopsisFinance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has madTrade Review“This important book is an unusually topical attempt to introduce readers to the relationship between the technical analysis of financial market prices and the automated implementation of its findings. The book will be of considerable interest to those who wish to know about this relationship in an eminently readable form: both professional financial market analysts and those considering future employment in the field.” --Michael Dempster, Professor Emeritus in the Statistical Laboratory at the University of Cambridge“AI is an important part of finance today. Students who want to join the finance industry should read this book. The trained eyes will also find a lot of insights in the book. I cannot think of any other book that teaches computational finance at a beginner's level but at the same time is useful to practitioners.” --Amadeo Alentorn, PhD, Head of Systematic Equities at Jupiter Asset Management"AI for Finance is an excellent primer for experts and newcomers seeking to unlock the potential of AI. The book combines deep thinking with a bird’s eye view of the whole field - the ideal text to get inspired and apply AI. A big thank you to Edward Tsang, a pioneer of AI and quantitative finance, for making the concepts and usage of AI easily accessible to academics and practitioners." --Richard Olsen, Founder and CEO of Lykke, co-founder of OANDA, and pioneer in high frequency finance and fintech“Without a doubt, AI symbolizes the future of finance and, in this important book, Professor Tsang provides an excellent account of its mechanics, concepts and strategies. Books featuring AI in finance are rare so practitioners and students would do well to read it to gain focus and valuable insights into this fast-evolving technology. Congratulations to Professor Tsang for providing a readable and engaging work in a complex technology that will appeal to all levels of readers!” --Dr David Norman, Founder of the TTC Institute"The use of AI/ML in the financial industry is now more than a hype. In financial institutions there are numerous active transformation programs to introduce AI/ML enabled products in areas such as risk, trading and advanced analytics. In this book, Edward, one of the early adopters of AI in finance, has provided an insightful guide for both finance practitioners and academics. I can see this book becoming a major reference in real-world applied AI in finance. Directional Change (Chapter 6) should be of particular interest to data scientists in finance, as how one collects data determines what one can reason about." -- Dr Ali Rais Shaghaghi, Lead Data Scientist at NatWest Group.Table of Contents1. AI-Finance Synergy, 2. Machine Learning Knows No Boundaries?, 3.Machine Learning in Finance, 4. Modelling, Simulation and Machine Learning, 5. Portfolio Optimization, 6. Financial Data: Beyond Time Series, 7. Over the Horizon
£114.00
Taylor & Francis Ltd A Primer on Machine Learning Applications in
Book SynopsisMachine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included.Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machiTable of Contents1. Introduction 2. Artificial Neural Networks 3. Fuzzy Logic 4. Support Vector Machine 5. Genetic Algorithm (GA) 6. Hybrid Systems 7. Data Statistics and Analytics 8. Applications in the Civil Engineering Domain 9. Conclusion and Future Scope of Work
£87.39
Taylor & Francis Ltd A First Course in Fuzzy Logic
Book SynopsisA First Course in Fuzzy Logic, Fourth Edition is an expanded version of the successful third edition. It provides a comprehensive introduction to the theory and applications of fuzzy logic.This popular text offers a firm mathematical basis for the calculus of fuzzy concepts necessary for designing intelligent systems and a solid background for readers to pursue further studies and real-world applications.New in the Fourth Edition: Features new results on fuzzy sets of type-2 Provides more information on copulas for modeling dependence structures Includes quantum probability for uncertainty modeling in social sciences, especially in economics With its comprehensive updates, this new edition presents all the background necessary for students, instructors and professionals to begin using fuzzy logic in its manyapplications in computer science, mathemaTable of ContentsThe Concept of FuzzinessExamples. Mathematical modeling. Some operations on fuzzy sets. Fuzziness as uncertainty.Some Algebra of Fuzzy SetsBoolean algebras and lattices. Equivalence relations and partitions. Composing mappings. Isomorphisms and homomorphisms. Alpha-cuts. Images of alpha-level sets.Fuzzy QuantitiesFuzzy quantities. Fuzzy numbers. Fuzzy intervals. Logical Aspects of Fuzzy SetsClassical two-valued logic. A three-valued logic. Fuzzy logic. Fuzzy and Lukasiewicz logics. Interval-valued fuzzy logic.Basic Connectivest-norms. Generators of t-norms. Isomorphisms of t-norms. Negations. Nilpotent t-norms and negations. T-conforms. De Morgan systems. Groups and t-norms. Interval-valued fuzzy sets. Type-2 fuzzy sets.Additional Topics on ConnectivesFuzzy implications. Averaging operators. Powers of t-norms. Sensitivity of connectives. Copulas and t-norms.Fuzzy RelationsDefinitions and examples. Binary fuzzy relations. Operations on fuzzy relations. Fuzzy partitions. Fuzzy relations as Chu spaces. Approximate reasoning. Approximate reasoning in expert systems. A simple form of generalized modus ponens. The compositional rule of inference.Universal Approximation Fuzzy rule bases. Design methodologies. Some mathematical background. Approximation capability. Possibility TheoryProbability and uncertainty. Random sets. Possibility measures. Partial KnowledgeMotivations. Belief functions and incidence algebras. Monotonicity. Beliefs, densities, and allocations. Belief functions on infinite sets. Mobius transforms of set-functions. Reasoning with belief functions. Decision making using belief functions. Rough sets. Conditional events.Fuzzy MeasuresMotivation and definitions. Fuzzy measures and lower probabilities. Fuzzy measures in other areas. Conditional fuzzy measures.The Choquet IntegralThe Lebesgue integral. The Sugeno integral. The Choquet integral. Fuzzy Modeling and ControlMotivation for fuzzy control. The methodology of fuzzy control. Optimal fuzzy control. An analysis of fuzzy control techniques.
£114.00
Cambridge University Press Cellular Neural Networks and Visual Computing
a huge range and FREE tracked UK delivery on ALL orders.
£75.04
Cambridge University Press OnLine Learning in Neural Networks
a huge range and FREE tracked UK delivery on ALL orders.
£40.84
Cambridge University Press Computer Simulation in Brain Science
a huge range and FREE tracked UK delivery on ALL orders.
£137.75
Cambridge University Press Cellular Neural Networks and Visual Computing
a huge range and FREE tracked UK delivery on ALL orders.
£120.65
Cambridge University Press OnLine Learning in Neural Networks 17 Publications of the Newton Institute Series Number 17
a huge range and FREE tracked UK delivery on ALL orders.
£126.35
Cambridge University Press Pattern Recognition and Neural Networks
a huge range and FREE tracked UK delivery on ALL orders.
£44.64
Cambridge University Press Networks Optimisation and Evolution 21 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 21
a huge range and FREE tracked UK delivery on ALL orders.
£63.64
Cambridge University Press Spiking Neuron Models Single Neurons Populations Plasticity
a huge range and FREE tracked UK delivery on ALL orders.
£59.84
