This paper describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g. stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement...

This paper describes iUBC, a neural network based approach that achieves competitive results on the interpretable STS task (iSTS 2016). Actually, it achieves top performance in one of the three datasets. iUBC makes use of a jointly trained classifier and regressor, and both models work on top of a recurrent neural network. Through the paper we provide detailed description of the approach, as we...

in this paper we propose a method for solving some well-known classes of lane-emden type equations which are nonlinear ordinary differential equations on the semi-in nite domain. the proposed approach is based on an unsupervised combined arti cial neural networks (ucann) method. firstly, the trial solutions of the differential equations are written in the form of feed-forward neural networks co...

Gaining the competitive advantage in every supply chain is dependent on the quality of the production and the desired cost. Therefore, Iran Khodro Company (IKCO) is critical need of decreasing extra costs through effective planning and cost management in order to stay in the competitive car market. The basis of each planning is knowledge and prediction. Due to high fluctuation of the amount of ...

In this chapter, we propose a new method to measure the importance of input variables and to examine the effect of the input variables on other components. We applied the method to competitive learning, in particular, self-organizing maps, to demonstrate the performance of our method. Because our method is based upon our information-theoretic competitive learning, it is easy to incorporate the ...

In this paper we present an improved neural network to solve strictly convex quadratic programming(QP) problem. The proposed model is derived based on a piecewise equation correspond to optimality condition of convex (QP) problem and has a lower structure complexity respect to the other existing neural network model for solving such problems. In theoretical aspect, stability and global converge...

An optical modeless Sign Language Recognition (SLR) system is presented. The system uses the HAusdorf-Voronoi NETwork (HAVNET), an artificial neural network designed for two-dimensional binary pattern recognition. It uses an adaptation of the Hausdorff distance to determine the similarity between an input pattern and a learned representation. A detailed review of the architecture, the learning ...

Abstract: In this research, at first, the natural frequencies of a cracked beam are obtained analytically, then, location and depth of a crack in beam is identified by neural network method. The research is applied on a beam with an open crack for three different boundary conditions. For this purpose, at first, the natural frequencies of the cracked beam are obtained analytically, to get the ex...

the hybrid fuzzy differential equations have a wide range of applications in science and engineering. we consider the problem of nding their numerical solutions by using a novel hybrid method based on fuzzy neural network. here neural network is considered as a part of large eld called neural computing or soft computing. the proposed algorithm is illustrated by numerical examples and the resu...

in this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. in proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. in comparison with existing similar neural networks proposed model provides solutions with high accuracy. numerica...