This book provides a comprehensive knowledge of the fundamental concepts and techniques in soft computing, which is a burning topic of research now-a-days in the field of computational intelligence along with some of their application to control and pattern recognition. Soft computing paradigms such as fuzzy logic system, neural networks and genetic algorithms are discussed in detail with many solved examples to facilitate the in-depth understanding of the methodologies.
Additional Info
  • Publisher: Laxmi Publications
  • Language: English
  • ISBN : 978-93-81159-66-8
  • Chapter 1


    Artificial intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, form machine vision to expert systems. The elements that the fields of AI have in common is the creation of machines that can “think”.
  • Chapter 2

    FUZZY LOGIC SYSTEMS Price 2.99  |  2.99 Rewards Points

    Fuzzy logic is an area of research based on the work of Lofti A. Zadeh. Fuzzy logic is used where a system is difficult to model exactly (but an inexact model is available), is controlled by a human operator or expert, or where ambiguity or vagueness is common. A typical fuzzy system consists of a rule base, membership functions and an inference procedure. Though fuzzy logic has been applied to many fields from control theory to artificial intelligence, it still remain controversial among most statisticians, who prefer Bayesian logic and some control engineers, who prefer traditional two-valued logic.
  • Chapter 3

    NEURAL NETWORKS Price 2.99  |  2.99 Rewards Points

    A neural network is a powerful data modelling tool that is able to capture and represent complex input/ output relationships. The motivation for the development of neural network technology stemmed form the desire to develop an artificial system that could perform “intelligent” tasks similar to those performed by the human brain
  • Chapter 4

    GENETIC ALGORITHMS Price 2.99  |  2.99 Rewards Points

    A genetic algorithms (or GA for short) can be stated as a programming technique that mimics biological evolution as a problem solving strategy. Given a specific problem to solve, the input to the GA is a set of potential solutions to that problem, encoded in some fashion, and a metric called a fitness function that allows each candidate to be quantitatively evaluated.

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