A Novel Optimization Mothed of Parameters Based on Combined NN and GA

Jiang Xingjun, Yao Linan Department of computer, Hunan Radio & TV University, Changsha, China

jxjyangli@yahoo.com.cn

Abstract

In this paper, an optimization system is established

based on a hybrid neural network and genetic algorithm approach. The application program is compiled in Matlab engineering computing language, which is used in calculating the parameter value predicted by neural network and the result of genetic algorithm optimization .The comparison and error analysis has been carried out between the results predicted by network and CAE simulated results, which shows that the BP network is stable and reliable. The optimized outcome verified by CAE simulation and tested by experiment has been proved to be correct. It has been bean indicated that the injection parameter optimization method based on the hybrid neural network and genetic algorithm approach is feasible. 1. Introduction

The Neural network (NN) is based on that the mankind are knowing the comprehension to the brain Neural network to construct up the artificial of, from many layers Neuron through conjunction but become, can carry out a certain function of, the mathematics model that theories turn, is according to a kind of information system of the mimicry brain Neural network structure and function but establishment[1].It is the complicated network that be linked by a great deal of simple component( called the Neuron) actually, can carry on the complicated logic operation and imitate complicated nonlinear system, realize the function of nonlinear mapping .The network of BP is a kind of network model that being used widely, it has functions such as self-organization, self-learning and associative memory, and it’s fault-tolerant ability is also excellent. It is the new generation information processing tool

[2].Under the condition of the neural quantity of the layer is enough, the network of BP that takes to have the deviation and have at least a layer to plus the line to output the layer can approach arbitrarily complicated nonlinear function [3-4].

Genetic algorithm (GA) is an algorithm that draw lessons from the natural choose of animate nature of height precede together, random, search algorithm from the orientation. Inherit the simple coding technique of algorithm exploitation and breed the mechanism to express the complicated phenomenon, resolve the complicated problem. It used the community manhunt technique, will grow the cluster to represent a problem solution, pass to the current kind cluster infliction choice, cross and a series of heredity of etc. of variation operation, produce the new generation to grow the cluster, and make grow the cluster to evolve the containment to look like the appearance of the superior solution gradually. Genetic algorithm express of be easy to the characteristics such as realization and strong sex etc., make it is in many realms, studying in the machine in recent years especially, the mode identify, the intelligence control and superior turn etc. the realm got the extensive application [5].

The applied admixture Neural network and the heredity calculate way methods carry on noting the craft parameter optimization to turn for acquiring the superior value in the craft parameter quickly and accurately, reducing the parameter to adjust to try time, reducing the waste of the artificial material, raising the work effect and producing the quantity to have the actual meaning[6]. This text approaches the hybrid neural network and genetic algorithm in the application of the craft parameter optimization. 2. The combined algorithm based on neural network and genetic algorithm

Applied neural network technique, can predict the exportation parameter according to the importation parameter quickly, but the result is the mapping value, not optimal value. To get the optimal value, we should carry on optimized calculation. Genetic algorithm is a kind of pass to imitate the nature evolution process to carry on random, search the superior solution from the orientation of method.

In inject model, adjust the molding tool temperature (Tmold), inject temperature of melt (Tmelt) or note time of (tinj) etc. the importation parameter of whichever, will model need of inject the pressure (Pinj) and cool off time etc. the creation influence. Inject the size of the pressure to have to affect very greatly towards involving the ware of the product quantity of internal help dint. Lead to inject the pressure to cause the internal help dint enlarge highly, lead of big remaining internal help dint and will cause the song of the piece of transform. The efficiency that cooling time (tconl) then affects to produce. Note in the fluxion, the max shear rate and shearing stress in the inner of the melt is also important factor that affects the quantity.

For this, use the input variable of the network as design variable

X= [ Tmold Tmelt tinj] Objective function:

)τ(τd)(γγ/c )/t(tb)/Pi(Paf(x):Minimize

maxmax

coolmaxcoolnjmaxinj

⋅⋅+⋅

+⋅+⋅=

Pinjmax ≤ Pcriticnl

γ max ≤ γ criticnl

τ max ≤ τ criticnl Is this characteristic of minimum value that begs the

target function value according to the problem that this text study, a function of adoptive orientation is:

Fil(f(X))=k-f(X) In the type:K is a can guarantee that a function of

orientation is a just when full greatly solid constant. After the race evolution of the first generation, in the

following generation, if the minimum orientation degree that grows the individual inside the cluster is small individual in preceding generation (the father generation) of the most suitable should degree, then make duplicate the latter to the son generation to grow the cluster inside, get the former eliminate, make moderation of grow the cluster evolve along with the heredity of carry on continuously to raise. Grow the scale of the cluster optimization to heredity the performance efficiency that turn and end and optimization turn the result contain influence. The scale is too small, optimization turn the function not good, be easy to sink into the part superior; the scale is big too, the calculating complexity and workloads increase quickly, influence evolution efficiency. Generally speaking, choosing of the bigger initial number scale is helpful to get global optimal solution, usually taking 20-100.

The coding method that we usually use is binary code, its advantage is that it’s coding and decoding can be operated simply, and heredity operation such as crossover and mutation can be easily realized. But

inconvenience in the particular knowledge of problem that reflection beg; don't keep the view to the performance of problem. While using long-lost to turn to the consecution function, should code the method existence to reflect to shoot the error margin, the individual code shorter can not reach the accuracy request, code longer make the manhunt space nasty play of algorithm extend again, the function of algorithm lower.

This literary grace uses to float to order number to code the method; meaning to grow the cluster in the individual of the data of each gene is a real amount.

Establish the individual number to grow the cluster for the n, the gene number of the each individual is a m, namely from a real amount composing that changes the quantity, the x mean t generation an individual, and have the x ∈ Rm then the x can mean for:X=(), the t generation grows a group of Xts, can mean for the matrix of the n × m:Xt=() T.Should code the method to express of change and measure meaning clear, explicit, don't need the decoding, have no long-lost reflect to shoot the error margin, will not produce to code the length of the string and the problem of the expression accuracies, also nonexistent because of coding the transformation but causing optimization to turn to search the nasty play of space aggrandizement, thus lower the problem of algorithm function, ability valid improvement heredity the complexity of algorithm, exaltation operation efficiency. 3. The realization of hybrid neural network and optimization of genetic algorithm

Making use of the nonlinear characteristic of the Neural network shoot the characteristic and heredity calculate way abreast, random, search the characteristic from the orientation, carry on the Neural network and heredity calculate way organic combine, carry out the craft parameter optimization to turn. This text built up is optimization to turn the system according to the craft parameter of the admixture neural network and the heredity calculates way method. Its process’s procedure as the figure 1 shows. 4. Example verification

In order to verify the result of system optimization, this text carried on example research. Optimization turn the beginning of the system to start to input the appropriate value that the parameter choice heredity calculates the son, grow a group of scale is 100, biggest evolution algebra is 200. The result and CAE that system is optimization to turns imitate the contrast

of verify the result to see the table 2 show, and to optimization the parameter that turn carried on the

experiment verification[7].

Fig. 1 The hybrid method based on NN and GA

Table 1 Contrast of the result between optimization system and CAE imitation

From the contrast between the Neural network

prediction and the result of CAE imitation in the table 1, we can see, after enough sample training of the result and CAE that Neural network predict imitate of as a result have to fit together sex goodly, the biggest and opposite error margin of four exportations of network item distinguish to 1.21%, 1.25%, 2.08%, 6.02%, higher accuracy of as a result have of the

estimate, the network structure design have good stable credibility. The study method of the choice dissimilarity, the speed that network training also will have large differences. This text uses the different study rule to carry on the network training on trial respectively, finally adopted algorithm of Levenbeng- Manquardt. Algorithm of L- M considered Newton's method and a method of the Gauss gradient at the same

parameter after system optimization Output item

Molding tool temperature

Temperature Inject time

Inject the pressure

Cool off time

Biggest shear to slice the velocity

Biggest shear to slice should dint

40.92� 269� 1.44s Mp s I/s Mp

Result of the system optimization 42.59 14.98 8293 0.88

Result of the CAE imitates 42.24 15.14 8237 0.92 Opposite error margin

-0.83% 1.06% -0.68% 4.35%

The beginning starts After renew of grow the cluster

Variation

Cross

Choice

Whether ?

Preprocessing

The BP network estimate Compute the

procedure

Postprocessing

A valuation of orientation

The result of output excellent turn

YES

time, comparing the pure steps a method of usage to descend the method, training the speed to want quickly have to have another [8-9].The piece of the layer Neuron compares to the accuracy that network train to affect greatly, the quantity is too little, the accuracy can not reach the request, the network does not refrain from rash action, the appropriate increment quantity contributes to the exaltation network accuracy.

Table 1 lists the result of parameter and output after system optimization. Using after system optimization the parameter that turn carries on the CAE emulation verification, turning result of verification with Optimization to output result carry on the contrast, can see two kinds of result relatively near to, the opposite error margin of four exportations item respectively is-0.83%,1.06%,-0.68% and 4.35%.Through using the different importation parameter value to carry on many times optimization and imitate the verification with CAE, the error margins are all small, shows that the result is right and dependable.

In experimental verification, carrying on inject to model the experiment with the craft parameter that the beginning starts to try the selection, often appear a to fill the dissatisfied circumstance or piece surfaces to appear the quantity problems, such as more obvious joint, the surface and the partial song...etc., adopt Optimization turn of parameter start experiment to gain of note the ware of and did not appear the above-mentioned blemish, after the examination, the quantity meets the request. 5. Conclusions

This text establishment of 33 number the importation, four output’s two layer BP network structures, after study train can estimate notes the parameter value more accurate and dependably. The artificial Neural network starts the dissimilarity of the power value matrix along with the random output beginning while carry on the study training, the learning process is also not same, but the network is end and will be tend into refrain from rash action, estimate of as a result jump together, the network has good dependability and steadiness.

The genetic algorithm real amount that this text design codes to keep the view strong, can be easily comprehended, the physics meaning of the codes the data can be understood clearly, did not need the decoding, the expression accuracy problem coding doesn’t exist. Compared with the binary-coded method, should code the method and can improve the complexity of genetic algorithm availably, rise to operate the efficiency, is a kind of simple, convenient ,practical and valid coding method.

This text establishment of according to admixture the Neural network and heredity algorithm method of Optimization turn the system to have efficiently sex and credibility, can turn towards noting the craft parameter to carry on Optimization availably, Optimization turn the result to was imitate by CAE verification and experiment verification, the certificate is right. That system can be used simply and conveniently, provide Optimization the craft parameter quickly, turn ability valid exaltation the work effect, decrease the waste of the manpower and material resources, have the actual engineering application value. Acknowledgement

This paper is supported by the Society Science Foundation of Hunan Province of China No. 07YBB239, and Hunan Science and Technology Agency Science and Technology Project (2008FJ3035). References [1] Weijin Jiang. A Novel Algorithm of Neural Network Optimized Design. Acta Electronica Sinica (Chinese Journal of Electronics), 2006, 15(4A): 925-928. [2] Jan Bazan. 1996. Dynamic reducts and statistical inference, Sixth International Conference on IPMU, pp.1147-1152. [3] Zhao Kai & Wang. A Reduction Algorithm Meeting Users' Requirements. Journal Computer Science and Technology. 2002,17(5): 578-593. [4] Jiang Weijin. Research on Method of Neural Network Optimized Design Based on Immune Modulated Symbiotic Evolution. Journal of Computational Information Systems, 2006, 2(4): 1317-1324 [5] Baogu W, Furong G, Polock Y. Neural Network Approach to Predict Melt Temperature in Injection Processes [J]. Chinese J. of Chem. Eng, 2000, 8(4):326-331. [6] Sivakumar B, Jayawardena A W, Fernando T M K G. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches [J]. Journal of Hydrology, 2002.(265):225-245. [7] Jiang Weijin. Research on Distributed Solution and Correspond Consequence of Complex System Based on MAS. Journal of Computer Research and Development, 2006, 43(9): 1615-1623