This paper discusses a method for training multi-layer perceptron networks called DMP2 (Dynamic Multi-layer Perceptron 2). The method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. The focus of this paper is on the effects of using multiple node types within the DMP framework. Simulation results s...

Necessary and sufficient conditions for implementing particular decision regions by multi-layer perceptrons have been presented in recent studies. In this paper, from a viewpoint of engineering, a constructive algorithm is proposed to implement celled decision regions using two-layer perceptrons without any training procedure. The algorithm examines the feasibility of a celled decision region a...

In the domain of artificial neural networks, learning process represents one most challenging tasks. Since classification accuracy highly depends on weights and biases, it is crucial to find its optimal or suboptimal values for problem at hand. However, a very large search space, difficult proper connection biases. Employing traditional optimization algorithms this issue leads slow convergence ...

The vision system provides an important way for construction robots to obtain the type and spatial location information of object. characteristics environment, object, robot structure are jointly examined in this paper propose approach object segmentation by spraying based on multi-layer perceptron. Firstly, hand-eye experimental platform is built through establishing mathematical model calibra...

The purpose of train detection systems is to check that related track section clear vehicles before a may be authorized pass through railroad. the important task for ensuring safety traffic. Multi-layer Perceptron classifier, which consists feedforward neural networks constructed multiple layers interconnected artificial neurons, proved effective trainset class classification in this study. Usi...