Weikuan Jia

Bio: Weikuan Jia is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Probabilistic neural network & Artificial neural network. The author has an hindex of 2, co-authored 2 publications receiving 42 citations.

An Improved BP Neural Network Algorithm Based on Factor Analysis.

Abstract: Back-Propagation (BP) neural network, as one of the most mature and most widespread algorithms, has the ability of large scale computing and has unique advantages when dealing with nonlinear high dimensional data. But when we manipulate high dimensional data with BP neural network, many feature variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the the accuracy of recognition finally. Factor analysis (FA) is a multivariate analysis method which transforms many feature variables into few synthetic variables. Aiming at the characteristics that the samples processed have more feature variables, combining with the structure feature of BP neural network, a FA-BP neural network algorithm is proposed. Firstly we reduce the dimensionality of the feature factor using FA, and then regard the features reduced as the input of the BP neural network, carry on network training and simulation with low dimensional data that we get. This algorithm here can simplify the network structure, improve the velocity of convergence, and save the running time. Then we apply the new algorithm in the field of pest prediction to emulate. The results show that under the prediction precision is not reduced, the error of the prediction value is reduced by using the new algorithm, and therefore the algorithm is effective.

Research of neural network algorithm based on FA and RBF

Abstract: This paper proposes a radial basis function (RBF) neural network algorithm based on factor analysis (FA-RBF) with the architecture feature of RBF network when the data are high-dimensional and complex. By reducing the feature dimension of the original data, FA-RBF algorithm regards the data after dimension reduction as the inputs of the RBF network, and then trains and simulates the network. The algorithm obviously simplifies the network architecture. By analyzing an example, the results show when the algorithm's predicted precision is not reduced, the convergence velocity is improved, the running time is saved and the error of the predicted value is reduced. In order to test and verify the validity of the new algorithm, we compare it with the RBF neural network algorithm based on principal component analysis (PCA-RBF), the predicted results of FA-RBF algorithm are better than the results of RBF and PCA-RBF algorithm.

Radial basis function neural networks: a topical state-of-the-artsurvey

Abstract: Abstract Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and fine tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.

A new optimized GA-RBF neural network algorithm

Abstract: When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

Using Genetic Algorithms to Optimize Artificial Neural Networks.

Abstract: Artificial Neural Networks (ANNs), as a nonlinear and adaptive information processing systems, play an important role in machine learning, artificial intelligence, and data mining. But the performance of ANNs is sensitive to the number of neurons, and chieving a better network performance and simplifying the network topology are two competing objectives. While Genetic Algorithms (GAs) is a kind of random search algorithm which simulates the nature selection and evolution, which has the advantages of good global search abilities and learning the approximate optimal solution without the gradient information of the error functions. This paper makes a brief survey on ANNs optimization with GAs. Firstly, the basic principles of ANNs and GAs are introduced, by analyzing the advantages and disadvantages of GAs and ANNs, the superiority of using GAs to optimize ANNs is expressed. Secondly, we make a brief survey on the basic theories and algorithms of optimizing the network weights, optimizing the network architecture and optimizing the learning rules, and make a discussion on the latest research progresses. At last, we make a prospect on the development trend of the theory.

A Case Study of Text-Based CAPTCHA Attacks

Abstract: CAPTCHA (Completely Automated Public Turing Test to tell Computers and Human Apart) is widely used than before, which becomes the common part of current website login system. However, the CAPTCHA implementation is tricky and risky without deliberate design. In this paper, we give a study case of the vulnerabilities in current login website using text-based CAPTCHA. Our target is a website of mainstream bank of china. We show that with some specialized methods, the CAPTCHA scheme in its website can be easily cracked. Finally, we give some advices for CAPTCHA designers to revise our CAPTCHA implementation security in the future.

Network Traffic Prediction based on Particle Swarm BP Neural Network

Abstract: The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.