Hybrid neural network

About: Hybrid neural network is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 18223 citations.

A New Improved Maxnet Based on a Hybrid Neural Network That Does Not Need to be Trained

Abstract: The maximum of a set is the element that is greater or equal to all the remaining ones. This seams obvious but it is this idea that is behind our Maxnet based on an Hybrid Neural Network with multiplication units. Although this approach does not need training it implies N2 hard limit perceptrons, N analog switches units and one linear neuron for a set of N elements. In a first approach we consider the simpler case where we only want to get the order of the input variable(s) that is/are the maximum(s), in a second approach we consider the case where we want to get the value(s) of the maximum(s), in a third approach we solve the same problem but with only one output introducing Mutual Inhibitio and finally we solve the same problem without Mutual Inhibition and introducing a division unit to divide the sum of all maximums by the number of maximums. Finally we compare our Maxnet with the recent published proposals and we show the great advantages of our approach either for software implementation or hardware realization.

Water quality evaluation model based on hybrid PSO-BP neural network

Abstract: A hybrid neural network algorithm, aims at evaluating water quality, based on particle swarm optimization (PSO) algorithm, which has a keen ability in global search and back propagation (BP) algorithm that has a strong ability in local search Heuristics has been proposed to optimize the number of neurons in the hidden layer The comparison with the traditional BP NN shows the advantage of the proposed method with high precision and good correlation The values of average absolute deviation (AAD), standard deviation error (SDE) and squared correlation coefficient (R 2 ) are 00072, 00208 and 098845, respectively The results show that the hybrid PSO-BP NN has a good predictal ability of evaluating water quality; it is a practical and efficacious method to evaluate water quality DOI : http://dxdoiorg/1011591/telkomnikav12i23190

On the Hybrid Neural Network Model for Solving Optimization Problems

Abstract: A recent model of neural networks, named the Hybrid Neural Network Model (HN), for solving optimization problems appeared in [3]. In [3], the main algorithm called the Hybrid Network Updating Algorithm (HNUA) is used to drive the HN model. The best thing about the HNUA is that it reaches a feasible solution very quickly. Our argument here is that while the HNUA is very quick to satisfy the constraints, it guarantees very little in terms of the quality of the generated solution. In this paper we rewrite one of the steps in the HNUA so that the goal function is better served. we demonstrate our work using the traveling salesman problem as an example.