Integration Of Neural Network, Fuzzy Logic & Genetic Algorithm

Introduction: -Hybrid systems employ more than one technology to solve a problem. Hybridization of technologies can have pitfalls and therefore need to be done with care.

• If one technology can solve a problem then a hybrid technology ought to be used only if its application results in a better solution.

• Hybrid systems have been classified as:

− Sequential hybrid system: the technologies are used in pipelining fashion;

− Auxiliary hybrid system: the one technology calls the other technology as subroutine;

− Embedded hybrid system: the technologies participating appear to be fused totally.

• Hybridization of fuzzy logic, neural networks, and genetic algorithms has led to creation of a perspective scientific trend known as soft computing.

− Neural networks mimic our ability to adapt to circumstances and learn from past experience,

− Fuzzy logic addresses the imprecision or vagueness in input and output,

− Genetic algorithms are inspired by biological evolution, can systemize random search and reach to optimum characteristics.

• Each of these technologies has provided efficient solution to wide range of problems belonging to different domains. However, each of these technologies has advantages and disadvantages. It is therefore appropriate that Hybridization of these three technologies are done so as to overcome the weakness of one with the strength of other.

Hybridization - Integration of NN, FL, and GA:-Fuzzy logic, neural networks and Genetic algorithms are soft computing methods which are inspired by biological computational processes and nature’s problem solving strategies.

 Neural Networks (NNs) are highly simplified model of human nervous system which mimics our ability to adapt to circumstances and learn from past experience. Neural Networks systems are represented by different architectures like single and multilayer feed forward network. The networks offer back proposition generalization, associative memory and adaptive resonance theory.

 Fuzzy logic addresses the imprecision or vagueness in input and output description of the system. The sets have no crisp boundaries and provide a gradual transition among the members and non-members of the set elements.

Genetic algorithms are inspired by biological evolution, can systemize random search and reach to optimum characteristics.

Each of these technologies have provided efficient solution to wide range of problems belonging to different domains. However, each of these technologies suffer from advantages and disadvantages.

 It is therefore appropriate that Hybridization of these three technologies are done so as to overcome the weakness of one with the strength of other.