Self-evolving Takagi-Sugeno-Kangfuzzy neural network with self-evolving forgetting factor

Abstract

Soft computing, a concept introduced by Zadeh[30], is in essence modeled after the human mind. Numerous studies have been done on the human cognitive process in attempts to understand the reasoning employed by humans as they try to solve complex problems. The results of these studies have lead to the development of a new branch of intelligent systems, systems that behave more so like humans. This new breed of systems exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solution cost. The major components of Soft Computing are Fuzzy Logic, Neural Network, Evolutionary Computing, Machine Learning and Probabilistic Reasoning. Using these components of Soft Computing in combination seem to deliver better results when solving real life problems, than if each component is used independently. ‘Neuro fuzzy computing’ is a prominent example of one such combination that has been particularly effective.

The capability to combine human-like reasoning of fuzzy systems together with the connectionist structure and learning ability of neural networks, makes neuro fuzzy computing a popular framework for solving problems in soft computing [31]. Neuro-fuzzy hybridization is also commonly known as fuzzy neural networks (FNN) or neuro-fuzzy systems (NFS). Being able to provide insights about the symbolic knowledge embedded within the network is the primary advantage of neuro-fuzzy systems [32], making it of immense use in commercial and industrial applications. Having such wide reaching applications makes it of great interest to those in various scientific fields of study.