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This paper innovatively proposes a hybrid intelligent system combining fuzzy comprehensive assessment approach and artificial neural network (ANN) that predicts the safety performance of construction site for breaking through the limitations of conventional method. And also inducts the sensibility analysis to discriminate the importance of each index in the assessment index system. The effectiveness...
Since correct prediction of bankruptcy prediction of a company is very important for investors, lenders and managers, most efforts have been done to improve the predictive capability of corporate bankruptcy prediction models. Most previous studies use corporate financial statement to do bankruptcy prediction. However, corporate performance is always affected by macroeconomic conditions. This study...
Objective Forecast and analysis of cerebral infraction incidence rate are the basis and key work of cerebral infraction prevention and control. At present, forecast of cerebral infraction incidence rate is mainly based on traditional research approach or single artificial neural network technology. Recent study results show that combined forecast model approach enjoys more precise forecast than monomial...
This paper presents an improved KIII stimulation model based on olfactory neural network (ONN) system. Taking the output responses of M1 nodes as benchmarks, the model chooses 8-channel KIII network. Analyses of both the methods for taking values of M1 nodes and the cross-connect weights among M1 nodes in different channels have provided us with an approach for taking response values of M1 nodes in...
Prediction of monsoon rainfall in a timely manner can be highly beneficial for Pakistan, where monsoon is the major source of rain. Presently, Multiple Linear Regression and Statistical Downscaling Models are being used for monsoon rainfall prediction. In spite of making use of a large number of resources and having dependency on a number of parameters, the results of these models have not been satisfactory...
The application of the operation of convolution based on artificial neural network processing for the problem of determination of thickness of layered medium layer by impulse irradiation is considered. The impulse fields reflected from the model of human body surface are analyzed by convolutional neural network in time domain directly. The normal incidence of plane wave with Gaussian time form on...
With the economic development of the emerging market countries, bankruptcy and financial crisis occur more and more frequently in business, credit and savings institutions, and thus the demand for enterprise financial crisis prewarning is rapidly growing. The main purpose of this paper to build a business financial crisis prewarning model based on BP neural network to conduct empirical analysis of...
Nowadays, the BP network algorithm has achieved a great success and many nonlinear problems can be solved well. However, standard BP network algorithm has some Shortcomings. Such as local minimum, low convergence and oscillation effects etc. GA has a strong macro-search capability. It has some advantages. Such as simple and universal, robust, parallel computing features, so use it to complete the...
In this paper, we present a spiking bidirectional associative memory (BAM) with temporal coding. The coding scheme used in artificial neural networks (ANN) known as “mean firing rate” cannot comply with the fast and complex computations occurring in the cortex. In biological neural networks the information is coded and processed based on the timing of action potentials. To improve the biological plausibility...
The link between neural activity and energy flows forms the basis of several forms of functional neuroimaging. Since the biophysics of neurovascular interactions is extremely complex, it would be worthwhile to investigate this question using simple computational models. Since neural networks are models of computation in the brain it would be interesting to study energy utilization in these models...
The accurate and reliable Trip-generation Forecasting Model is the most basic and important part of the traffic forecasting model. This paper focuses on combining the neural network which has a strong fitting capability and genetic algorithm which has an excellent Global search capability with trip-generation forecasting model in order to achieve the purpose of improving the accuracy of prediction...
Modeling neural tissue is an important tool to investigate biological neural networks. Until recently, most of this modeling has been done using numerical methods. In the European research project "FACETS" this computational approach is complemented by different kinds of neuromorphic systems. A special emphasis lies in the usability of these systems for neuroscience. To accomplish this goal...
Model building methodologies are playing an increasingly significant role in many aspects of software engineering activities. Today models are being applied right from requirement conceptualization to the final software installation and maintenance. Traditional methodologies however, fail to cope with increasing complexity and rapidly evolving nature of the software. The need for an efficient model...
Construction of intelligent transportation system is a necessary requirement for the development of transport, and LPR(License Plate Recognition) is an important part of construction of intelligent transportation system, Therefore, the research of license plate recognition method is of importance. The license plate character is recognized by building BP artificial neural network in this paper, it...
Neural Networks are models of biological neural structure, so the scientist, engineers & mathematicians etc. try to make an intellectual abstraction with the help of neural network which would enable a computer work in a similar fashion in which the human brain works. Here we use a specific type of neural network called ??Holographic Neural Network?? (HNN), for stock price prediction. HNN takes...
Recently, the identification of biological neural networks has been reformulated as an optimization problem based on a framework of adaptive synchronization. In this paper, four different optimization algorithms, including genetic algorithm, jumping gene genetic algorithm (JGGA), tabu search, and simulated annealing, have been applied for this optimization problem. Based on the simulation results,...
On the basis of analyzing the significance of assessing operation capability in SMB, the Appraisal-index system of operation capability for SMB is built, and appraisal model is established using BP neural network. The conjunction weights of the neural network are continuously modified layer by layer from output layer to input layer in the process of neural network training to reduce the errors between...
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...
In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier...
The IEEE Computational Intelligence Society (CIS) focuses on computational and theoretical aspects of mimicking nature for problem solving. CIS core technologies include neural, fuzzy, and evolutionary computation, as well as hybrid intelligent systems that contain these and other related paradigms. The Society has its own history of transformation from the Neural Network Council to the Neural Network...
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