Structural Learning with M-Apoptosis in Neurofuzzy GMDH

Training Neurofuzzy Networks with Participatory Learning

This paper introduces a new approach to adjust a class of neurofuzzy networks based on the idea of participatory learning. Participatory learning is a mean to learn and revise beliefs based on what is already known or believed. The performance of the approach is verified with the Box and Jenkins gas furnace modeling problem, and with a shortterm load forecasting problem using actual data. Compa...

Compensatory neurofuzzy systems with fast learning algorithms

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuz...

Reinforcement learning in neurofuzzy traffic signal control

Evolutionary Reinforcement Learning for Neurofuzzy Control

Disadvantages of traditional reinforcement learning techniques are complicated structures and that training algorithms are often reliant on the derivative information of the problem domain and also require a priori information of the network architecture. Such handicaps are overcome in this paper with the use of ‘messy genetic algorithms’, whose main characteristic is a variable length chromoso...

Cloning for Heteroscedasticity Elimination in GMDH Learning Procedure

Selection procedure • The initial state form n inputs only, there are no neurons. • If there are k neurons already, the probability of a selection from inputs and from neurons is given by constant probability p0 that one of the network inputs will be selected. • Otherwise an already existing neuron is selected randomly with probability proportional to fitness. • After the new neuron is formed a...