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The traditional neural network is unavoidable to present local extreme value question, may result in failing training. On the basis of quantization of weapon system safe index, it has adopted neural network based on improved genetic algorithm to set up the systematic safety evaluation model of the weapon. It utilizes improved genetic algorithm to optimize the weight of neural network and get the final...
This paper presents a stereo matching algorithm utilizing vertical disparity (SMAVD) in solving the matching problem of stereo vision. SMAVD adopts a two-dimensional Hopfield neural network (HNN) to match the stereo pairs according to the energy function developed to describe three constraints including uniqueness, similarity and compatibility. The similarity of one matched pair is measured according...
According to the combustion control system of industry boiler with large time delay and nonlinear, a kind of combustion system of industry boiler based on GA PID neural network controller design method is proposed in this paper. The boiler model is given and the comparison is made between GA PID neural network controller and the traditional PID controller. The simulation results indicate that the...
Though several approaches have already been proposed in the literature to evolve neural network topologies for solving a wide range of machine learning tasks, this paper presents an alternative one, capable of evolving arbitrarily connected feed forward neural networks (ACFNNs), including linear and nonlinear neurons. A genetic algorithm is conceived to adjust the topology and also to perform variable...
When a male silk moth senses sexual pheromone of a female partner by using its antenna, it repeats certain series of walking pattern and arrives to the partner. This walking pattern is generated in Lateral Accessory Lobe (LAL) and the ventral protocerebrum (VPC) domain which controls physical exercise. Therefore, in this study, we elucidate the process of this behavior by constructing a neural network...
This paper studies the dynamic binary neural network having N bits input, N bits output and ternary weighting parameters of the hidden layer. Applying feedback from the output to the input, the network can generate dynamic binary sequence. We presents a simple learning algorithm that uses the genetic algorithm and reduces the number of hidden neurons efficiently. Performing a basic numerical experiment,...
Interdisciplinary problem solving in computational biology requires a fundamental understanding of complex biological adaptive systems, from cellular to molecular level in order to tackle challenging problems such as neurodegenerative diseases. In this work we present a description and an initial evaluation of a Spiking Neural Network-Genetic Algorithm (SNN-GA) system we are developing for the computational...
In this analysis a process to demarcate areas with analogous wind conditions is shown. For this purpose a dispersion graph between the wind directions will be traced for all stations placed in the studied zone. These distributions will be compared among themselves using the centroids extracted with SOFM algorithm. This information will be used to build a matrix, allowing us working simultaneously...
In this paper, three methods for developing the optimal topology of feed-forward artificial neural networks are described and applied for modeling a complex polymerization process. In the free radical polymerization of styrene, accompanied by gel and glass effects, the monomer conversion and molecular masses are modeled depending on reaction conditions. The first proposed methodology is an algorithm...
Radial basis function Networks (RBFNs) have been successfully employed in different Machine Learning problems. The use of different radial basis functions in RBFN has been reported in the literature. Here, we discuss the use of the q-Gaussian function as a radial basis function employed in RBFNs. An interesting property of the q-Gaussian function is that it can continuously and smoothly reproduce...
In the present study, input-output relationships of metal inert gas welding process have modeled using radial basis function neural networks. As the performance of a neural network depends on its structure and parameters, some approaches have been developed to optimize them simultaneously. The performances of the developed approaches have been compared among them on some test cases. It has been observed...
Data classification is a prime task in data mining. Accurate and simple data classification task can help the clustering of large dataset appropriately. In this paper we have experimented and suggested a simple ANN based classification models called as minimal ANN (MANN) for different classification problems. The GA is used for optimally finding out the number of neurons in the single hidden layered...
Application of online system identification based on improved quantum-behaved particle swarm optimization is studied in this paper. QPSO algorithm combined with the single neuron can improve the local search capabilities and identification accuracy. Then the improved QPSO is applied to online identify parameters of a system described by differential equations and compared with the improved particle...
This paper presents a connectionist approach using back-propagation artificial neural network (BP) and genetic algorithm (GA) to predict the mechanical properties of ceramic die material. This method is using GA to optimize BP, including how to train the initial connection weights of network and to determine the threshold values. In the analysis, mechanical properties of ceramic die material are optimized...
Soft computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Segmentation is an essential...
In the paper the method of creating investment strategies for a profitable trading system is described. This method is based on artificial intelligence techniques and technical analysis tools. Created strategies describe the investment signal and amount of cash or stocks, which should be used at a given moment. The carried out experiments allow to find values of parameters for generating investment...
In this paper, we analyze the characteristics of the dynamic job shop scheduling problem when machine breakdown and new job arrivals occur. A hybrid approach involving neural networks (NNs) and genetic algorithm(GA) is presented to solve the dynamic job shop scheduling problem as a static scheduling problem. The objective of this kind of job shop scheduling problem is minimizing the completion time...
Forecasting currency exchange rates is an important issue in finance. This topic has received much attention, particularly in econometrics and financial selection of variables that influence forecasts. In this paper, a new forecasting model is constructed: we adopt a Genetic Algorithm (GA) to provide the optimal variables weight and we select the optimal set of variables as the input layer neurons,...
When the holes to be machined are mass produce with numerical control machine tool, the empty routing will numerous and the process is inefficient. The machining routing in quick process was proposed in this paper and the optimizing mathematical models for process routing in machining a group of holes was established. A new method was presented by combined improved genetic algorithm (GA) with Elman...
Unmanned vehicle intelligent control methods are advantaged in this paper. For path control of an unmanned vehicle, tracking method is proposed based on neural network. A neural network is made through experiments. Neural network's input are velocity, friction coefficient, hope radius, output is velocity difference. Then prevision control method is used to steering control. This neural network control...
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