evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...

The prospect of widespread deployment of autonomous vehicles invites the reimagining of the multiagent systems protocols that govern traffic flow in our cities. One such possibility is the introduction of micro-tolling for fine-grained traffic flow optimization. In the micro-tolling paradigm, different toll values are assigned to different links within a congestable traffic network. Self-intere...

One may argue that the simplest type of neural networks beyond a single perceptron is an array of several perceptrons in parallel. In spite of their simplicity, such circuits can compute any Boolean function if one views the majority of the binary perceptron outputs as the binary output of the parallel perceptron, and they are universal approximators for arbitrary continuous functions with valu...

the stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. this paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (mdnn) and studies the stability of this algorithm. also, stable learning algorithm for parameters of ...

In this paper, we develop a curved search algorithm which uses second-order information, for the learning algorithm for a supervised neural network. With the objective of reducing the training time, we introduce a fuzzy controller for adjusting the first and second-order approximation parameters in the iterative method to further reduce the training time and to avoid the spikes in the learning ...

the large amount of computation necessary for obtaining time optimal solution for moving a manipulator on specified path has made it impossible to introduce an on line time optimal control algorithm. most of this computational burden is due to calculation of switching points. in this paper a learning algorithm is proposed for finding the switching points. the method, which can be used for both ...

A resurgent interest for grammatical inference aka automaton learning has emerged in several intriguing areas of computer sciences such as machine learning, software engineering, robotics and internet things. An algorithm commonly uses queries to learn the regular grammar a Deterministic Finite Automaton (DFA). These are posed Minimum Adequate Teacher (MAT) by learner (Learning Algorithm). The ...

Currently the most popular learning algorithm for connectionist networks is the generalized delta rule (GDR) developed by Rumelhart, Hinton & Williams (1986). The GDR learns by performing gradient descent on the error surface in weight space whose height at any point is equal to a measure of the network's error. The GDR is plagued by two major problems. First, the progress towards a solution us...

The Helmholtz machine is a new unsupervised learning architecture that uses top-down connections to build probability density models of input and bottom-up connections to build inverses to those models. The wake-sleep learning algorithm for the machine involves just the purely local delta rule. This paper suggests a number of different varieties of Helmholtz machines, each with its own strength...