Description

Book Synopsis

This book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the GA-ANN model.

Features:

  • A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm and neural network using MATLAB)
  • A detailed description of the hybrid intelligent technique called the adaptive fuzzy inference technique (ANFIS)
  • Formulation of the nonlinear model like analysis of ANOVA and response surface methodology
  • Variety of solved problems on ANOVA and RSM
  • Case studies of above mentioned intelligent techniques on the different process control systems<

    Table of Contents

    Part A: Artificial Intelligence & Cognitive Computing : Theory &Concept

    1. Introduction to AI. 2. Practical Approach of Fuzzy Logic Controller. 3. A Practical Approach to Neural Network Models. 4. Introduction to Genetic Algorithm. 5. Modeling of ANFIS (Adaptive Fuzzy Inference System) System. 6. Machine Learning Techniques for Cognitive Modeling

    Part B: Artificial Intelligence & Cognitive Computing : Practices

    7. Parametric Optimization of N Channel JFET using Bio Inspired Optimization Techniques. 8. AI based Model of Clinical and Epidemiological Factors for COVID19. 9. Fuzzy Logic Based Parametric Optimization Technique of Electro Chemical Discharge Micro-Machining (µ-CDM) Process during Micro-Channel Cutting on Silica Glass. 10. Study of ANFIS model to Forecast the Average Localization Error (ALE) with Applications to Wireless Sensor Networks (WSN). 11. Performance Estimation of Photovoltaic Cell using Hybrid Genetic Algorithm & Particle Swarm Optimization. 12. Bio inspired Optimization based PID Controller Tuning for a Non-Linear Cylindrical Tank System. 13. A Hybrid Algorithm Based on CSO & PSO for Parametric Optimization of Liquid Flow model. 14. Modelling of Improved Deep Learning Algorithm for Detection of Type 2 Diabetes. 15. Human Activity Recognition (HAR), Prediction & Analysis using Machine Learning

Artificial Intelligence for Cognitive Modeling

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    £120.00

    Includes FREE delivery

    Order before 4pm tomorrow for delivery by Tue 9 Jun 2026.

    A Hardback by Pijush Dutta, Souvik Pal, Asok Kumar

    1 in stock


      View other formats and editions of Artificial Intelligence for Cognitive Modeling by Pijush Dutta

      Publisher: CRC Press
      Publication Date: 4/19/2023 12:00:00 AM
      ISBN13: 9781032105703, 978-1032105703
      ISBN10: 1032105704

      Description

      Book Synopsis

      This book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the GA-ANN model.

      Features:

      • A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm and neural network using MATLAB)
      • A detailed description of the hybrid intelligent technique called the adaptive fuzzy inference technique (ANFIS)
      • Formulation of the nonlinear model like analysis of ANOVA and response surface methodology
      • Variety of solved problems on ANOVA and RSM
      • Case studies of above mentioned intelligent techniques on the different process control systems<

        Table of Contents

        Part A: Artificial Intelligence & Cognitive Computing : Theory &Concept

        1. Introduction to AI. 2. Practical Approach of Fuzzy Logic Controller. 3. A Practical Approach to Neural Network Models. 4. Introduction to Genetic Algorithm. 5. Modeling of ANFIS (Adaptive Fuzzy Inference System) System. 6. Machine Learning Techniques for Cognitive Modeling

        Part B: Artificial Intelligence & Cognitive Computing : Practices

        7. Parametric Optimization of N Channel JFET using Bio Inspired Optimization Techniques. 8. AI based Model of Clinical and Epidemiological Factors for COVID19. 9. Fuzzy Logic Based Parametric Optimization Technique of Electro Chemical Discharge Micro-Machining (µ-CDM) Process during Micro-Channel Cutting on Silica Glass. 10. Study of ANFIS model to Forecast the Average Localization Error (ALE) with Applications to Wireless Sensor Networks (WSN). 11. Performance Estimation of Photovoltaic Cell using Hybrid Genetic Algorithm & Particle Swarm Optimization. 12. Bio inspired Optimization based PID Controller Tuning for a Non-Linear Cylindrical Tank System. 13. A Hybrid Algorithm Based on CSO & PSO for Parametric Optimization of Liquid Flow model. 14. Modelling of Improved Deep Learning Algorithm for Detection of Type 2 Diabetes. 15. Human Activity Recognition (HAR), Prediction & Analysis using Machine Learning

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