Robots have always been touted as powerful tools that could be used effectively in a
number of applications ranging from automation to human-robot interaction. In order for such …

This paper presents a three level architecture for an autonomous agent and its application to
the navigation problem in an unknown environment. The architecture is structured in three …

This chapter presents neuro-fuzzy networks for nonlinear process modelling and control.
The neuro-fuzzy network uses local linear models to model a nonlinear process and the …

In this paper, we present a fast, hybrid gradient descent and genetic algorithm for training
recurrent neural networks. The hybrid algorithm uses the strengths of genetic algorithm and …

An automaton fixes the connections among functional states in a process. If the functional
states are obtained by clustering, non all classes or functional states are defined by the …

In this paper, we define the notions of max-min deterministic general fuzzy automata of order
n of a max-min general fuzzy automaton, the overall transition function of a max-min …

This paper establishes a learning paradigm for natural language. The paradigm is then
effectively modeled by using a hybrid approach to Artificial Intelligence (AI). A synergy of …

The paradigm of deterministic nite-state automata (DFAs) and their corresponding regular
languages have been shown to be very useful for addressing fundamental issues in …

For better fuzzy signal processing, this paper presents a new processing tool-fuzzy
automata, discusses mainly how the fuzzy automata infer the fuzzy logic, and how the fuzzy …

First-order recurrent neural networks can be trained to recognize strings of a regular
language. Finite state automata can be extracted from these neural networks. Normally, a …