One popular learning algorithm for feedforward neural networks is the back.propagation (BP) algorithm which includes parameters: learning rate (1]), momentum factor (n) and steepness parameter (A.).The appropriate selections of these parameters have a large effect on the convergence of the algorithm. Many techniques that adaptively adjust these parameters have been developed to increase speed o...

One popular learning algorithm for feedforward neural networks is the backpropagation (BP) algorithm which includes parameters, learning rate (eta), momentum factor (alpha) and steepness parameter (lambda). The appropriate selections of these parameters have large effects on the convergence of the algorithm. Many techniques that adaptively adjust these parameters have been developed to increase...

Ahstruet -A cooperative game playing learning automata model is presented for learning a complex nonlinear associative task, namely learning of Boolean functions. The unknown Boolean function is expressed in terms of minterms, and a team of automata is used to learn the minterms present in the expansion. Only noisy outputs of the Boolean function are assumed to be available for the team of auto...

In this paper, we study the ability of learning automata-based schemes in escaping from local minima when standard backpropagation (BP) fails to 2nd the global minima. It is demonstrated through simulation that learning automata-based schemes compared to other schemes such as SAB, Super SAB, Fuzzy BP, adaptive steepness method, and variable learning rate method have a higher ability to escape f...

Deteministic finit state (DFA) automata have emerged as an effective tools for agent modeling applications. The problem of automata learning is to determine a DFA from a series of observation and has recently been studied extensively and a number of algorithms has been proposed. These algorithms can be divided into groups : supervised and unsupervised . In supervised algorithms, we have access ...

This paper introduces a new model, i.e. state-coupled replicator dynamics, expanding the link between evolutionary game theory and multiagent reinforcement learning to multistate games. More precisely, it extends and improves previous work on piecewise replicator dynamics, a combination of replicators and piecewise models. The contributions of the paper are twofold. One, we identify and explain...

Learning automata is a foundational problem in computational learning theory. However, even efficiently learning random DFAs is hard. A natural restriction of this problem is to consider learning random DFAs under the uniform distribution. To date, this problem has no non-trivial lower bounds nor algorithms faster than brute force. In this note, we propose a method to find faster algorithms for...

This paper presents a general approach to image segmentation and object recognition that can adapt the image segmentation algorithm parameters to the changing environmental conditions. Segmentation parameters are represented by a team of generalized stochastic learning automata and learned using connectionist reinforcement learning techniques. The edge-border coincidence measure is first used a...

Techniques for learning automata have been adapted to automatically infer assumptions in assume-guarantee compositional verification. Learning, in this context, produces assumptions and modifies them using counterexamples obtained by model checking components separately. In this process, the interface alphabets between components, that constitute the alphabets of the assumption automata, are fi...

In this paper, we will investigate two partial orders on classical states∆n that induce a domain theoretic structure. In particular, we will look at the Bayesian order presented by Coecke and Martin (2002) and the implicative order presented by Martin (2004). For both of the domains that arise from these orderings on the classical states, Shannon entropy is a measurement in the domain theoretic...