Soft Computing/Control Strategies
Cheng-Hung Chen, D. Subbaram Naidu · 2017
This chapter introduces some soft computing (SC) or computational intelligence (CI) strategies involving fuzzy logic (FL), neural network (NN), adaptive neuro-fuzzy inference system (ANFIS), tabu search (TS), genetic algorithm (GA), particle swarm optimization (PSO), and developed adaptive particle swarm optimization (APSO). Humans are flexible and can adapt to unfamiliar situations and they can get information in an efficient manner and discard irrelevant details. A fuzzy set is a set without a sharp (crisp) boundary or without binary characteristics in contrast to a classical set. Fuzzy sets are mathematical objects modeling this impreciseness and use the concept of degrees of membership function to give a mathematical definition of fuzzy sets. Inspired by biological nervous systems, neural network (NN) is typically composed of a set of parallel and distributed processing units, called nodes or neurons. These are usually ordered into layers, appropriately interconnected by means of unidirectional weighted signal channels, called connections or synaptic weights.