IE3004 Applied Soft Computing Syllabus:

IE3004 Applied Soft Computing Syllabus – Anna University Regulation 2021

OBJECTIVES

• the paradigm of soft computing techniques
• Genetic algorithms, its applications and advances.
• Neural Networks, architecture, functions and various algorithms involved.
• Fuzzy Logic, Various fuzzy systems and their functions.
• Design of hybrid methodology to solve optimization problems

UNIT I INTRODUCTION

History and Applications of Artificial Intelligence – Algorithmic versus Heuristic reasoning, Representation and Intelligence. Knowledge Representation: Rule based, Model based, Case based and hybrid systems. Logic based Abductive Inference, Stochastic approach to uncertainty.

UNIT II GENETIC ALGORITHMS

Introduction to Genetic Algorithms (GA) : Reproduction, Cross over, Mutation – Applications andsoftware –– Intelligent Agents – Multiple Agents and Data Mining – Distributed Artificial Intelligence.

UNIT III NEURAL NETWORKS

Machine Learning Using Neural Network, Adaptive Networks – Feed forward Networks – Supervised Learning Neural Networks – Radial Basis Function Networks – Reinforcement Learning – Unsupervised Learning Neural Networks – Adaptive Resonance architectures.

UNIT IV FUZZY LOGIC

Crisp set versus Fuzzy Sets – Operations on Fuzzy Sets –Fuzzy Arithmetic – Fuzzy Relations – Membership Functions- Fuzzy Rules and Fuzzy Reasoning – Fuzzy Inference Systems – FuzzyExpert Systems – Fuzzy Decision Making.

UNIT V HYBRID SYSTEMS

Adaptive Neuro-Fuzzy Inference Systems – Hybrid intelligence systems – Opportunistic Scheduling and Pricing Strategies for Automated Contracting in Supply Chains – AHPANP – SEM – DEA .

TOTAL: 45 PERIODS
OUTCOMES

CO1: Recognize the feasibility of applying a soft computing methodology for a particular problem
CO2: Apply genetic algorithms to combinatorial optimization problems
CO3: Apply neural networks to pattern classification problems
CO4: Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems
CO5: Design hybrid system to revise the principles of soft computing in various applications

TEXT BOOKS:

1. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing”,Prentice-Hall of India, 2003.
2. George J. Klir and Bo Yuan, “Fuzzy Sets and Fuzzy Logic-Theory and Applications”, PrenticeHall, 1995.
3. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and Programming Techniques”, Pearson Edn., 2003.

REFERENCES:

1. Mitchell Melanie, “An Introduction to Genetic Algorithm”, Prentice Hall, 1998.
2. David E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, AddisonWesley, 1997.
3. Jacek M. Zurada, “Introduction to Artificial Neural Systems”, PWS Publishers, 1992.
4. Prasad, Bhanu (Ed.), Soft Computing Applications in Business Series: Studies in Fuzzinessand Soft Computing, Vol. 230, 2010
5. Aliev, Rafik Aziz, Fazlollahi, Bijan, Aliev, Rashad Rafik, Soft Computing and its Applications inBusiness and EconomicsSeries: Studies in Fuzziness and Soft Computing, Vol. 157, 2004