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Training of parameters in RBF neural network, this article proposes an optimization of RBF neural network parameters algorithm, which can overcome the disadvantages of select of data center and weights in RBF neural network. The algorithm process input data normalization and compute network output and hidden layer output angle cosine firstly, a set of data being established as the network center when...
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,...
In order to improve the correct rate of transformer fault diagnosis based on three-ratio method of traditional dissolved gas analysis (DGA), a novel intelligent transformer fault diagnosis method based on both DGA and probabilistic neural network (PNN) was proposed. In this fault diagnosis method, it takes three characteristic values of the improved three-ratio method as its inputs and five transformer...
Three indicators (R, I30, P), and all four indicators (R, I30, P, I) of erosive rainfall in Jia Zhaichuan small watershed of Song county are chosen respectively as the input vector to predict sedimentation volume with the two neural network of RBF and BP, and fit with the actual values. The results testify the fitting and predicted effects of RBF neural network are all better than BP network, as well...
This paper aims to develop intelligent Predictive Monitoring Emission Systems (PEMS) for three distinct case studies involving traffic, gasoline fuel tanks and large combustion plants (LCP). The underlying theme of pollutant emissions exists in all three case studies whereby the gases that are monitored are NO2, unburned hydrocarbons, and SO2. These pollutants can cause grievous harm to health, environment...
Transmission line protection is an important issue in power system engineering because 85-87% of power system faults are occurring in transmission lines. This paper presents a technique to detect and classify the different shunt faults on a transmission lines for quick and reliable operation of protection schemes. Discrimination among different types of faults on the transmission lines is achieved...
Automatic facial expression analysis is the most commonly studied aspect of behavior understanding and human-computer interface. The main difficulty with facial emotion recognition system is to implement general expression models. The same facial expression may vary differently across humans; this can be true even for the same person when the expression is displayed in different contexts. These factors...
Elevators play an important role in today urban life. The elevator group control (EGC) problem is related to many factors, such as stochastic traffic states, the number of customers, running condition, and it is difficulties in analysis, design and control. In order to increase the elevators running efficiency and quality of service, the optimizing control strategy of elevators is studied in this...
In this paper radial basis function neural network (RBFNN) is used to extract total harmonics in converter waveforms. The methodology is based on p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the harmonics over a wide operating range are extracted. The proposed RBFNN filtering training algorithms are based on an efficient training method called hybrid learning method...
In this paper the feasibility of artificial neural network technology for air fine particles pollution prediction of main traffic route was discussed. The concentration data of PM2.5, PM5 and PM10 were measured in Zhongshan road, the main traffic route of Chongqing, China. Parameter Φ of emission capacity of motor vehicles was used as the independent variable of prediction model. RBF and BP neural...
This research work presents supervised Artificial Intelligence based control technique for an inverted pendulum. The inverted pendulum system is a classic control problem that is used in research. It is a suitable process to test prototype controllers due to its high non-linearities and lack of stability. Most traditional controllers (feedback linearisation, rule based control) are based around an...
The dye wastewater is an important environmental problem. Coagulation-flocculation process is an available technology in treatment of this kind of wastewater. The aim of the present study was to develop a model for coagulation-flocculation process using ferric chloride as a flocculant and the model could provide an alternative to the experimental jar test for determining the operational variables...
In this paper, we present a novel dynamic scheme to estimate the execution time of stored procedures inside the Database Management Systems (DBMS). Our estimation model can provide users an accurate estimation of the execution time of stored procedures. The proposed estimation model adopts the basic idea of Radial Basis Function (RBF) network to accurately predict the execution time of the stored...
An optical fiber displacement sensor based on Radial Basis Function neural network is proposed for enhancing accuracy and linear range. A Nearest Neighbor Clustering algorithm suitable for training RBF neural network in optical fiber displacement sensor is studied and implemented. The work method and process of sensor are described. Experimental results show that neural network method has higher precision...
In this paper two different neural network architectures are investigated for enough accurate field strength prediction in the complex indoor environment. The investigation includes multilayer perceptron (MLP) and radial basis function (RBF) neural networks. It has been already shown for neural networks as powerful tool in RF propagation prediction. Standard empirical or deterministic field prediction...
Parkinson's Disease (PD) is the second most common neurodegenerative action and expected to increase in the next decade with accelerating treatment costs as a consequence. This situation leads us towards the need to develop a Decision Support System for PD. In this paper we propose different methods based on evolutionary algorithms and RBF neural networks for diagnosis of PD. Three different evolutionary...
As testing the marine large-scale low-speed two stroke engine to determine the engine performance map for different working conditions costs too much time and money. So the prediction of the marine engine exhaust emissions modelling is developed to define how the inputs affect the outputs. The marine engine exhaust emissions were measured for different engine loads conditions. Using Radial Basis Function...
Electricity market demands to the power industry in de-regulated form in this paper. The proposed load forecasting using ANN shows the effective risk management plans. This power market is to maintain their effective cost in terms of energy generation, energy purchase and optimization of the switching losses. This creates the need of load forecasting. So in this paper the load forecasting using ANN...
Selecting the optimal parameters of the Support Vector Machines (SVM) is very important in practice. This paper detailedly analyzes the effects given by the Radial Basis Function (RBF) kernel parameter on the feature space, and proposes a novel kernel parameter evaluating method, which is based on the Inter-Class Mean Distance (ICMD). Theoretical and experimental analysis is made on the proposed method...
Considering about the fault features of traction machine for lifts, the basic characteristics of faults types are analyzed. By detecting vibration signals from vibration sensors, uses wavelet packet to decompose fault signal, extracts the signal characteristics of 8 frequency components from the low-frequency to high frequency in the third layer. The 8 obtained eigenvalues as the fault signals are...
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