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Tax assessment is an important and complex task in tax administration. A large number of data is involved in the process. Hence, a scientific model is in demanding. In this paper, we present a model which integrates the ant colony algorithm into artificial neural network to improve the performance of neural network in judgment of whether the taxpayer is credible. In details, we use ant colony algorithm...
Slot leakage electromagnetic field is a complex electromagnetic theory problem, and it's very difficult to exactly describe the distribution of field in analytic way. As a result, the analysis of electromagnetic interference (EMI) becomes the difficult point of electromagnetic compatibility (EMC) field. This paper will take the DC motor controller for electric forklift as the experimental circuit...
This article established artificial neural networks based on improved ant colony optimization evaluation model for residential performance. Firstly, on the basis of comprehensive analysis of the effects factors of residential building's performance, considering of the advantages of dealing with non-linear object of neural network, the neural network is trained by the sample data. While training neural...
Knowledge discovery has become of great importance owing to ever-increasing amounts of data collected by large organizations. This paper presents a knowledge discovery method called ant system, which is implemented by improving ant colony algorithm. Through specific application it has been proved that the algorithm is practical in knowledge discovery , and is a good method of knowledge discovery which...
The garbage crusher is one of the important parts in recoverable coal production line. To diagnose its faults during the working process, back propagation algorithm is used. However, it has some shortcomings, such as low precision solution, slow searching speed and easy convergence to the local minimum points. To overcome this problem, a novel method which integrates back propagation neural network...
For the particular controlled object AUV, a novel controller based on the fuzzy B-Spline neural network is presented, which embodies the merits of qualitative knowledge representation capability of fuzzy logic, quantitative learning ability of neural networks, as well as the excellent local controlling ability of B-Spline basis functions. However, to overcome the inherent deficiencies in the fuzzy...
Recently, cost-sensitive data mining has been an area of extensive research interests. Intelligent ant colony classification algorithm is introduced in cost-sensitive data mining method in order to obtain satisfied classification results by interaction of ant individuals. The convergence rate of classification is increased by using of metacost's meta-learning theory. Moreover, Boosting theory is investigated...
We used the ant colony pheromones in the intelligent learning. Proposed an intelligence network based on ant colony pheromones and TSP problem. The ant colony pheromones was in hierarchical distribution in the network, Recording of the learning and related information by the residual pheromones, so realized intelligent decision. We use the path choosing in the ant colony algorithm as decision of the...
According to the problems of the nonlinearity and non norm on dam displacement prediction, the dam displacement mode based on improved ant colony algorithm neural networks was proposed. The binary ant colony algorithm has been brought into the optimization of weights in neural networks. So that the shortcomings of the ant algorithm using in the combinatorial optimization in continuous field have been...
In order to forecast GDP growth much more accurate, a hybrid intelligent system is applied to improve the precision of forecasting, which combines ant colony clustering algorithm (ACCA) and RBF neural network. At first, we can make use of ACCA to cluster the data. And then, this clustered data is used to develop classification rules and train RBF neural network. The effectiveness of our methodology...
Introducing the rank-weight method into the basic ant colony optimization (ACO), we use the modified ACO to optimize the weights and thresholds value of neural networks (NN). And when the BPNN is being trained, this method can solve the disadvantages of running into the minimum easily, and enhance the convergence speed. So we get a heuristic method, which is good at time efficiency and derivation...
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