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The choice of optimal topology of neural network (NN) is one of the most important factor for the success of any application. Generally the optimization of neural network (NN) has based on cross validation method which requires more learning and test procedures. This paper proposes the use of sophisticated methods, it is one of the pruning NN methods as: "Optimal Brain Damage" (OBD) and...
Recent advances in microarray technology allow an unprecedented view of the biochemical mechanisms contained within a cell. Deriving useful information from the data is still proving to be a difficult task. In this paper a novel method based on a multi-objective genetic algorithm that discovers relevant sets of genes and uses a neural network to create rules using the evolved genes is described. This...
Sample database was established and the mapping relationship between span,rise-span ratios and type of shell with minimum weight was simulated by using BP neural network method. The selected typical samples were chosen from hundreds of sectional optimized results based on sequential two-level algorithm from five typical types of reticulated domes. This paper provides a simple lectotype optimization...
Extension neural network is a new method based on Extenics and neural networks, it is full use of the Extension of qualitative and quantitative description of the advantages, but also consider the parallel structure characteristics, of neural network. This article describes the extension theory and neural network fusion extension neural network structure and introduce ENN algorithm based on genetic...
A CMAC (Cerebellar Model Articulation Controller) network combined with PID (Proportion Integration Differential) control method was proposed in this paper. Aiming at the characteristics such as multivariable, strong coupling, nonlinear and time-varying parameters for the turbine regulating system, an improved mutative scale chaotic optimization algorithm was used for tuning the weight parameters...
Sample database was established and the mapping relationship between span,rise-span ratios and type of shell with minimum weight was simulated by using BP neural network method. The selected typical samples were chosen from hundreds of sectional optimized results based on sequential two-level algorithm from five typical types of reticulated domes. This paper provides a simple lectotype optimization...
The prediction of fill levels in stormwater tanks is an important practical problem in water resource management. In this study state-of-the-art CI methods, i.e., Neural Networks (NN) and Genetic Programming (GP), are compared with respect to their applicability to this problem. The performance of both methods crucially depends on their parametrization. We compare different parameter tuning approaches,...
Serial numbers identification of RMB (the name of Chinese paper currency) is a nonlinear and high dimensions pattern recognition problem which sample is limited. It is one of many difficulty problems in pattern recognition. It also has great research and practical value. This thesis studies the multi-class optimize algorithm in statistical learning theory, analyzes SMOD algorithm and its precondition...
In this paper we used a generalized net which gives a possibility for parallel optimization of multilayer neural networks. For training the backpropagation algorithm with momentum was considered. We proposed a generalized net model of parallel training of two neural networks with different architectures. The difference between the networks is in the number of neurons in main difference of the neural...
Aiming at the disadvantages of the standard Particle Swarm Optimization (PSO), a new particle swarm optimization algorithm based on dual mutation(DDPSO) is proposed. By comparing and analyzing the results of several Benchmark functions, the excellent performance of PSO is proved. The improved PSO is applied to optimize the structure and parameters in artificial neural network(ANN). The availability...
Intelligent sensor selection for monitoring operations is one of the serious subjects to reduce information processing time and increase information fusion accuracy. This paper attempts to design an intelligent sensor selection service by using optimization algorithm and neural networks. This service specifies the best group of sensors having the highest recognition rate in each situation. The important...
In this paper, the quality control of plastic gears manufacture, on which various factors are influential, is analyzed as a systematic engineering. Applying FEA techniques and optimization methods, with the study of plastic Gear forming process theory and realization of the process of numerical simulation, injection molding process parameters are optimized based on based on CAE, neural networks and...
The selection for the number of hidden nodes for a neural network is of critical importance. This paper proposes a novel algorithm to determine the number of hidden nodes of a neural network and optimize it. In the method, the number of hidden nodes H is first computed by empirical formulas, and the range of H is determined according to computed result. Then, the "three points search" is...
The angle of break is a key factor that determines the mining damage extent of the surface in a mine, and it is also used to depict the characteristics of the mining subsidence basin. The geological and mining factors that influence the angle of break are fully analyzed. Based on the practical observational data from the ground movement monitoring stations of many mines in China, a neural network...
A new particle swarm optimization algorithm with dynamically changing inertia weight and threshold value based on improved adaptive particle swarm optimization is proposed, in which the inertia weight of the particle is adjusted adaptively based on the premature convergence degree of the swarm and the fitness of the particle. The diversity of inertia weight makes a compromise between the global convergence...
This paper presents a novel technique for the supervised training of feed-forward artificial neural networks (ANN) using the Harmony Search (HS) algorithm. HS is a stochastic meta-heuristic that is inspired from the improvisation process of musicians. Unlike Backpropagation, HS is non-trajectory driven. By modifying an existing improved version of HS & adopting a suitable ANN data representation,...
Feed-forward artificial neural networks (FFANN) can be trained using genetic algorithm (GA). GA offers a stochastic global optimization technique that might suffer from two major shortcomings: slow convergence time and impractical data representation. The effect of these shortcomings is more considerable in case of larger FFANN with larger dataset. Using a non-binary real-coded data representation...
We present an attempt to separate between two kinds of events, using Genetic Algorithms. Events were produced by a Monte Carlo generator and characterized by the most discriminant variables. For the separation between events, two approaches are investigated. First, discriminant function parameters and neural network connection weights are optimized. In a multidimensional search approach, hyper-planes...
Considering the problem of the local optimization in the adaptive genetic algorithm (AGA), this paper presents an improved adaptive genetic algorithm (IAGA) which can optimize the weights and thresholds of the neural network. A stock prediction system based on neural networks and fuzzy theory is designed. According to the analysis of the history data of the stock, the system predicts this stock's...
We consider the multi-class classification problem, based on vector observation sequences, where the conditional (given class observations) probability distributions for each class as well as the unconditional probability distribution of the observations are unknown. We develop a novel formulation that combines training with the quality of classification that can be obtained using the 'learned' (via...
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