The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired

Abstract

This chapter follows the development of a class of intelligent information systems called evolving neuro-fuzzy systems (ENFS). ENFS combine the adaptive/evolving learning ability of neural networks and the approximate reasoning and linguistically meaningful explanation features of fuzzy rules. The review includes fuzzy expert systems, fuzzy neuronal networks, evolving connectionist systems, spiking neural networks, neurogenetic systems, and quantum inspired systems, all discussed from the point of few of fuzzy rule interpretation as new knowledge acquired during their adaptive/evolving learning. This review is based on the author's personal (evolving) research, integrating principles from neural networks, fuzzy systems and nature. Early work on the integration of neural networks and fuzzy systems for knowledge engineering: Neuro-fuzzy expert systemsThe seminal work by Lotfi Zadeh on fuzzy sets, fuzzy rules and intelligent systems [36][37][38] opened the field for the creation of new types of expert systems that combined the learning ability of neural networks, at a lower level of information processing, and the reasoning and explanation ability of fuzzy rule-based systems, at the higher level. An exemplar system is shown in Figure 22.1, where at a lower level a neural network (NN) module predicts the level of a stock index and a fuzzy reasoning module combines the predicted values with some macro-economic variables, using the following types of fuzzy rules [18]:IF <the predicted by the NN module stock is high> AND <the economic situation is good> THEN <buy stock> (22.1) These fuzzy expert systems continued the development of the hybrid NN-rulebased expert systems that used crisp propositional and fuzzy rules [13,15,17]. They represented a major topic at some conferences (Figure 22.2).

Assessment of the comparative efficiency of software‐based Boolean, electronic, software‐based fractional value and simplified quantum principal expert systems

“…Combined neural networks and expert systems have been proposed (Medsker, 1994), as has the use of neural network learning techniques with expert systems (Gallant, 1995). Kasabov (2013) discussed the similarities in purpose between software‐based Boolean expert systems and the various system types that have evolved from similar roots, including neural networks and quantum‐inspired systems.…”