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This article is to report a study based on fabric physical properties measured on the KES system. Grey incidence (GI) analysis, as a mathematic method that ranks the sequence of importance of lots of variables in complicated factors has been applied, In order to select the efficient input variables of ANN (artificial neural network) during the prediction of wearing comfort performance. A series of...
In order to reduce muzzle disturbance of gun, establish virtual prototype of a type of howitzer based on complex factors such as mass eccentricity of recoil parts, soil stiffness and damping, clearance of elevating mechanism, stiffness and damping of papilionaceous spring. Use velocity and angular velocity to reflect muzzle disturbance, then examine, simulate and analyze the virtual prototype. According...
In recent years, computer - aided design approach based Artificial Neural Network has been introduced to microwave modeling, simulation & optimization. In this paper, a neural network model is proposed for the design of spiral strip monopole antenna fed by a coplanar waveguide (CPW) for radio frequency identification (RFID) applications is presented. The designed antenna, which, including the...
A method based on ant colony algorithm (ACA) is proposed to train weights and thresholds for Back-propagation (BP) neural network. BP algorithm has been widely used in training artificial neural network (ANN). This algorithm has many attractive features, such as adaptive learning, self-organism, and fault tolerant ability. All of them make BP one of the most successful algorithms in various fields...
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...
The BP feed-forward neural network is popular in solving many non-linear multivariate and complex problems. The most important problem with neural network is to decide optimal structure and parameter settings. Literature presents a multitude of methods but there is no rigorous and accurate analytical method. This paper presents the hybrid approach of genetic algorithm and neural network computing...
Three influencing factors (roasting temperature, roasting time, and metal ratio) which affect the preparation conditions of Mn-Ce catalysts for catalytic wet air oxidation was investigated. A BP artificial neural network model was established, in which the input conditions were selected as roasting temperature, roasting time, and metal ratio, and the output condition was TOC removal of n-butyric....
Neural network learning methods provide a robust approach to approximating real-valued, discrete-valued and vector-valued target functions. Artificial neural networks are among the most effective learning methods currently known for certain types of problems. But BP training algorithm is based on the error gradient descent mechanism that the weight inevitably fall into the local minimum points. genetic...
As the widespread modus operandi in real applications, backpropagation(BP) in recurrent neural networks (RNN) is computationally more powerful than standard feedforward neural networks. In principle, RNN can implement almost any arbitrary sequential behavior. However, there are many drawbacks in BP network, for instance, confinement in finding local minimum and may get stuck at regions of a search...
As one of the extensive applications of artificial neural network, BP algorithm has some shortcomings such as local optimum. In this paper, we propose a new method--TACO-BP algorithm to train neural network, which may overcome the shortcoming. Firstly, we give description about the TACO-BP. After experiments, we compare the performance between TACO-BPNN and BPNN. Lastly, we analyze the results of...
Machining burrs are often created on the workpiece edges in micro-milling. Burrs make troubles on production lines in terms of deburring cost, quality of products and automation. To prevent problems caused by burrs in machining, prediction and control of burr size is desirable. Experimental studies show that burr formation in micro-milling is a highly complex process depending on a number of parameters...
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