Interpretability and learning in neuro-fuzzy systems

Abstract

A methodology for the development of linguistically interpretable fuzzy models from data is presented. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the ÿrst phase, the structure of the model is obtained by means of subtractive clustering, which allows the extraction of a set of relevant rules based on a set of representative input-output data samples. In the second phase, the parameters of the model are tuned via the training of a neural network through backpropagation. In order to attain interpretability goals, the method proposed imposes some constraints on the tuning of the parameters and performs membership function merging. In this way, it will be easy to assign linguistic labels to each of the membership functions obtained, after training. Therefore, the model obtained for the system under analysis will be described by a set of linguistic rules, easily interpretable.

Balanced the Trade-offs Problem of ANFIS using Particle Swarm Optimization

“…The interpretability is important issues in ANFIS process because it is affecting the complexity and processing time of the system. Based on [1], interpretability can be improved by fine-tuning the fuzzy rules with regularisation such as growing and pruning fuzzy rule number to find the effective one from all possible fuzzy rules in neuro-fuzzy structure.…”

“…Improving the approximation accuracy and interpretability of fuzzy systems is an important issue either in fuzzy systems theory or in its applications [1]. An adaptive neuro-fuzzy inference system (ANFIS) based on TSK model is a specific approach of neuro-fuzzy that has shown significant results in classification problem.…”

A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

“…(e) Use of possibilities of gradient and evolutionary methods for reduction and scaling of fuzzy rules and fuzzy sets (see e.g. [39,45,46,63]). (f ) Use of extended structures of neuro-fuzzy systems in purpose to increase both the accuracy and interpretability of neuro-fuzzy rules (see e.g.…”

Designing simulated annealing and subtractive clustering based fuzzy classifier

“…In work of Han et al [25], the input membership functions of Fuzzy Neural Network are firstly obtained with fuzzy space partition, and then the SC algorithm is utilized to get kernel rules and the importance of every rule. After constructing the rule base of neuro fuzzy system by the SC, similar membership functions are merged in order to remove the redundant rules [26]. Zhao et al [27], used particle swarm optimization algorithm to find the optimal membership functions (MFs), which are initially found by the SC, and consequent parameters of the rule base.…”