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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...
The problem of construction the neuronetworking systems for non-stationary information adaptive processing at various practical applications is formulated. The developed methods and algorithms of neural network training subset formation allow to take into account the conditions of information transfer, variation of statistical parameters and dynamic properties of data. The controlling algorithms which...
Finding the optimal parameters in anionic co-doped titanium dioxide (TiO2) is an important task in the compound preparation on either photocatalytic-oriented or mechanical-preferred properties. This work proposes a neural network-based system to optimize the process parameters of the deposition of TiCxOyNz films. The proposed system comprises three stages, which are data processing, parameter training...
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...
For better predicting and optimizing the blasting parameters in underground deep-hole mining, 16 groups of deep-hole blasting parameters are collected and collated, combining rough set and artificial neuron network theory, an optimized model for basting parameters in underground mines' long-hole caving based on rough set and artificial neural network is set up. Adopting the rough set software for...
In this paper, the exponential synchronization problem of a class of chaotic delayed neural networks (DNNs) via impulsive control method is studied. Based on the theory of impulsive functional differential equations (FDEs), some new synchronization criteria expressed in the form of linear matrix inequalities (LMIs) are derived. The designed impulsive controller not only can globally exponentially...
Heuristic constructive algorithms have been widely and successfully applied to the solution of routing problems. Since they generally consist of an iterative insertion of nodes to construction routes, prioritization rules for assignments is critic for algorithm's performance. Developing these rules is time consuming and relies much on researcher skills and knowledge on problem features. This paper...
Fast convergence-rate, low computation complexity and good stability are important goals in the researching area of neural network learning algorithm. A kind of parallel computing lagged-start hybrid optimization algorithm is studied, it not only integrates the basic gradient method and the unconstrained optimization algorithm to realize the supplement of their advantages, but also makes full use...
Trained speed of model based on traditional BP neural network was slowly and produced emanative result. A novel land evaluation model based on neural network with genetic optimization algorithm was presented in this paper. The neural network of model is front-network which comprised with five layers architecture which composed of dynamic inference with fuzzy rules where the consequent sub-models are...
Various hardware implementations of neural networks have been studied well in recent years. We have already proposed a hardware implementation method for neural network with a network on chip (NoC) architecture. A mapping of a neural network on NoC should be tuned to achieve high performance whenever neural network application is changed, so that different mapping methods are needed every time and...
In this paper, an intelligent prediction approach based on the neural networks rough set of a Genetic Selection Strategy Particle Swarm Optimization Algorithm(GSS-PSO) is proposed to measure the risky area caused by slope. With this approach, the attribute reduction method based on neighborhood rough set is adopted to conduct the attribute reduction, then the genetic strategy is used to reform the...
In Chinese township enterprises, multi-dimensional piece-rate wages calculation are very common. Because of the diverse types of products and the multi-dimensional floating price, the automatic wages-calculation system is needed. Considering that the rapid development of markets, thus, it is very important that the wages settlement systems to be carried out the adjustment and optimization of dynamic...
Extracting polybrominated biphenyls (PBB) from electronic and electrical equipment is of high concern due to RoHS directive. PBBs were so toxic for human and environment, they are applied largely in electronic and electrical equipment as flame retardant. In this thesis, a new method was developed to predict the optimal conditions of semivolatile organic polybrominated biphenyls (PBB) extraction from...
This paper presents a new BP neural network (BP NN) forecast model named IPSO-BP forecast model that is based on an improved particle swarm optimization (IPSO). The improved PSO employs parameter with crossover operator and mutations operator to significantly improve the performance of the original PSO algorithm in global search and fine-tuning of the solutions. This study uses the IPSO algorithm...
In many practical applications, new training data is acquired at different points in time, after a classification system has originally been trained. For instance, in face recognition systems, new training data may become available to enroll or to update knowledge of an individual. In this paper, a neural network classifier applied to video-based face recognition is adapted through supervised incremental...
This paper proposes a new approach to optimally determine the appropriate size and location of a dispatchable large Distributed Resource (DR) in a large mesh connected system. Inserting a DR in an already existing distribution system is an important issue at present, specifically under the deregulated electricity market. Determining the optimal siting and/or sizing of the DR is the key factor in determining...
Some important issues on the determination of weights in multi-objective decision making (MODM) and multiple learning machines system (MLMS) are discussed: it is presented that determining weights is an important evaluation process; some drawbacks of several current methods of determining weights with the help of optimization are analyzed. Meanwhile, it is pointed out that it's not overall and unreliable...
This paper extends the neural network based algorithm for equiripple design of higher-order digital differentiators in the weighted least-squares sense. The proposed approach formulates an error representation reflecting the difference between the desired amplitude response and the designed response in a Lyapunov error function. The optimal filter coefficients are obtained when neural network achieves...
In order to increase the accuracy of the cost estimates in government investment, a method based on the PSO trained neural network to estimate the cost is proposed. First the neural network model of a project cost estimate is created, and then PSO is introduced to optimize the weight and threshold of the neural network, at last the neural network trained is used to estimate cost of the project. The...
The purpose of this paper is to improve the risk evaluating quality of engineering item. The topology structure of evolutionary algorithm based BP (EABP) neural network is described, the principle of EABP neural network is introduced,and the implement step of EABP neural network is given. The combination algorithm is applied to risk evaluating for the engineering item, and its result is compared with...
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