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The goal of this project is thus to experiment with ANNs and to evaluate performance of ANN models in studying stock price patterns in time by attempting to predict future results of a time-series by simply studying patterns in the time-series of stock prices. In this project we have instantiated the proposed Neural Network using the stock prices of Iran Tractor Manufacturing Company during two years...
This paper focused on experimental data and study for the testing of the material mechanical properties using vibration technique. By applying vibration analysis and testing on the material, we could determine the natural frequencies, the damping ratio and mode shapes of the structure. However, in this study, we only considering the natural frequencies of the material as the input data needed for...
The aim of this research is to develop an intelligent automated online forecasting of a car fuel consumption using neural network and classified it into classes of driving style. A new online monitoring tool was developed to acquire and analyze data collected from a car for the purpose of fuel consumption modelling and forecasting. The data was transmitted via ECU Can Bus attach to the car to the...
Harmonic estimation is considered the most crucial part in harmonic mitigation process in power system. Artificial intelligent based on pattern recognition techniques is considered one of dependable methods that can effectively realize highly nonlinear functions. In this paper, a radial basis function neural network (RBFNN) is used to dynamically identify and estimate the fundamental, fifth harmonic,...
There are some shortages of knowledge acquisition and inefficency in ES. So, combines ES with ANN to construst military equipment fault diagosis expert system. Introduces the neural network learning system, the knowledge base and the reasoning mechanism of the expert system. After introducing ANN and ES, utilizing the adapting, self-learning abilities of ANN, methods of knowledge acquirement and representation...
Aiming at the difficulty of tank unit combat formation recognition in virtual simulation training, the recognition method based on BP neural network is put forward. After analyzing the definition and character of the tank unit combat formation, the recognition strategy for tank unit formation is put forward. Then the recognition model based on BP neural network is built. In order to get plentiful...
Established the computational model about the safe distance of vehicles. In order to simulate the dynamic model of rear-end, based on VB software to build a freeway rear-end simulation system. Simulation system provides an important means for in-depth study on rear-end probability. To investigate the non-linear relationship of probability and impact factors of rear-end, established probability of...
A forecasting model for gas emission based on wavelet neural network is proposed in this paper. In the model, wavelet neutral network (WNN) is applied to the forecasting with gradient descent and amended by validity of iteration training algorithm. Compared with back-propagation neural networks, forecasting of the model has advantages of faster convergence and more accurate. Simulation results have...
The generalization ability of neural network is an important aspect affecting its application. Meanwhile, the selection of training samples has a great impact on this ability. In order to improve the completeness of training samples, a method of samples self-learning of BP neural network based on clustering is put forward in this paper. By using the method of clustering, new samples can be collected...
Wind power prediction is of great importance for the safety, stabilization and economic efficiency of electric power grids, especially when the wind power penetration level of the gird is high. ANN (Artificial Neural Network) is an appropriate method for wind power prediction. But the generalization of common ANN is poor and the prediction precision is not stable. Neural network ensemble can enhance...
Following a number of studies that have employed different forms of neural network models to perform dissolved gas-in-oil analysis (DGA) of transformer bushings, this manuscript focuses on evaluating the relevance of the parameters that form part of the model input space. Using a multilayer neural network initially populated with all the 10 input parameters (10V-Model), a matrix containing causal...
This paper discusses on the adaptive neural network model for predicting the energy consumption at a metering station. The function of the metering system is to calculate the energy consumption of the outgoing gas flow. To ensure the robustness of the developed model, it is suggested to make the model an adaptive model that will periodically update the weights. This will ensure the reliability of...
Improving the diversity of Neural Network Ensembles (NNE) plays an important role in creating robust classification systems in many fields. Several methods have been proposed in the literature to create such diversity using different sets of classifiers or using different portions of training/feature sets. Neural networks are often used as base classifiers in multiple classifier systems as they adapt...
Artificial Neural Networks (ANNs) have been used as a promising tools for many applications. In recent years, a computer-aided design approach based on ANNs has been introduced to microwave modeling, simulation and optimization. In this work, the characteristics parameters of the conductor-backed asymmetric coplanar waveguide (CB - ACPW) with one lateral ground plane have been determined with the...
Radio Frequency microelectromechanical system (RF MEMS) is a relatively new field which has generated a tremendous amount of excitement because of its performance enhancement and low manufacturing cost. RF MEMS switch is a fundamental device that offers wide applications in defense and telecommunication systems. In this paper we propose an efficient approach based on Artificial Neural Network (ANN)...
This paper takes a kind of ceramic glaze as an example, and builds an improved BP neural network model for optimizing the formulation on ceramic glaze. The improved BP neural network adopts Levenberg-Marquardt algorithms. The paper reviews how to build the ceramic formulation optimization model based on BP artificial neural network, including the establishment of neural network, the training, and...
This paper proposed the heart disease diagnosis system using nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of Normal and several heart diseases based on heart sounds. In classification, a spectrogram was applied to the modeled heart sounds for features extraction and selection. The features were fed to the FFNN and trained using Resilient Backpropagation...
This paper proposes that we can improve the accuracy of the distance between ZigBee with the algorithm of BP neural network. Firstly, we analyze the wireless signaling path loss model, the principle of measuring distance based on RSSI, and the algorithm of BP neural network fully. Secondly, we get the experimental data from the hardware platform of ZigBee. Finally, we use the algorithm of BP neural...
This paper proposed the heart disease modeling system based on heart sounds. The model uses ARX model as regression vector and Neural Network as nonlinear model structures. The number of hidden neurons was optimised by minimizing the criterion of NSSE, fit and FPE criterion. The model architecture of 2-4-1 perfectly fits the original heart sound signals with average R-square of above 99.9%. The weight...
Artificial Neural networks ANNs are dynamic systems which have the ability not only to capture the relationship between input and output parameters of complex systems but also highly effective when there is no any mathematical formula or model for the system. Therefore, they are very potential and appropriate for design of systems whose functions cannot be expressed explicitly in the form of mathematical...
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