• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.31 no.3
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• pp.199-218
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• 2005

Purpose of Study: Peripheral nerve regeneration depends on neurotrophism of distal nerve stump, recovery potential of neuron, supporting cell like Schwann cell and neurotrophic factors such as BDNF. Peripheral nerve regeneration can be enhanced by the conduit which connects the both sides of transected nerve. The conduit maintains the effects of neurotrophism and BDNF produced by Schwann cells which can be made by gene therapy. In this study, we tried to enhance the peripheral nerve regeneration by using calcium phosphate coated porous conduit and BDNF-Adenovirus infected Schwann cells in sciatic nerve of rats. Materials and Methods: Microporous filter which permits the tissue fluid essential for nerve regeneration and does not permit infiltration of fibroblasts, was made into 2mm diameter and 17mm length conduit. Then it was coated with calcium phosphate to improve the Schwann cell adhesion and survival. The coated filter was evaluated by SEM examination and MTT assay. For effective allogenic Schwann cell culture, dorsal root ganglia of 1-day old rat were extracted and treated with enzyme and antimitotic Ara-C. Human BDNF cDNA was obtained from cDNA library and amplified using PCR. BDNF gene was inserted into adenovirus shuttle vector pAACCMVpARS in which E1 was deleted. We infected the BDNF-Ad into 293 human mammary kidney cell-line and obtained the virus plaque 2 days later. RT-PCR was performed to evaluate the secretion of BDNF in infected Schwann cells. To determine the most optimal m.o.i of BDNF-Ad, we infected the Schwann cells with LacZ adenovirus in 1, 20, 50, 75, 100, 250 m.o.i for 2 hours and stained with ${\beta}$-galactosidase. Rats(n=24) weighing around 300g were used. Total 14mm sciatic nerve defect was made and connected with calcium phosphate coated conduits. Schwann cells$(1{\times}10^6)$ or BDNF-Ad infected Schwann cells$(1{\times}10^6)$ were injected in conduit and only media(MEM) was injected in control group. Twelve weeks after surgery, degree of nerve regeneration was evaluated with gait analysis, electrophysiologic measurements and histomorphometric analysis. Results: 1. Microporous Millipore filter was effective conduit which permitted the adhesion of Schwann cells and inhibited the adhesion of fibroblast. We could enhance the Schwann cell adhesion and survival by coating Millipore filter with calcium phosphate. 2. Schwann cell culture technique using repeated treatment of Ara-C and GDNF was established. The mean number of Schwann cells obtained 1 and 2 weeks after the culture were $1.54{\pm}4.0{\times}10^6$ and $9.66{\pm}9.6{\times}10^6$. 3. The mRNA of BDNF in BDNF-Ad infected Schwann cells was detected using RT-PCR. In Schwann cell $0.69\;{\mu}g/{\mu}l$ of DNA was detected and in BDNF-Adenovirus transfected Schwann cell $0.795\;{\mu}g/{\mu}l$ of DNA was detected. The most effective infection concentration was determined by LacZ Adenovirus and 75 m.o.i was found the most optimal. Conclusion: BDNF-Ad transfected Schwann cells successfully regenerated the 14mm nerve gap which was connected with calcium phosphate coated Millipore filter. The BDNF-Ad group showed better results compared with Schwann cells only group and control group in aspect to sciatic function index, electrophysiologic measurements and histomorphometric analysis.