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Search Results: 1 - 10 of 27693 matches for " Flexible RBF neural network (F-RBF) "
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On Structure Design for RBF Neural Network Based on Information Strength

HAN Hong-Gui,QIAO Jun-Fei,BO Ying-Chun,

自动化学报 , 2012,
Abstract: Based on the systemic investigation on the feedforword neural network, for the problem of the structure design of the RBF neural network, a new flexible structure design method is used for RBF neural network in this paper. By computing the output-information (OI) of the hidden neurons and the multi-information (MI) of the hidden nodes and output nodes, the hidden nodes in the RBF neural network can be inserted or pruned, thus the topology of the network can be modulated. This method can effectively solve the structure design of the RBF neural network. The grad-descent method for the parameter adjusting ensures the exactitude of the flexible RBF neural network (F-RBF). The structure of the RBF neural network is self-organizing, and the parameters are self-adaptive. In the end, the proposed F-RBF is used for approximating the classical non-linear functions and modelling key parameters of the wastewater treatment process. The results show that the F-RBF obtains a favorable dynamic character response and the approximating ability. Especially, comparied with the minimal resource allocation networks (MRAN), the generalized growing and pruning RBF (GGAP-RBF) and the self-organizing RBF (SORBF), the proposed algorithm is more effective in terms of training time, generalization, and neural network structure.
Linear Discriminant Analysis F-Ratio for Optimization of TESPAR & MFCC Features for Speaker Recongnition
K. Anitha Sheela,K. Satya Prasad
Journal of Multimedia , 2007, DOI: 10.4304/jmm.2.6.34-43
Abstract: This paper deals with implementing an efficient optimization technique for designing an Automatic Speaker Recognition (ASR) System, which uses average F-ratio score of TESPAR(Time Encoded Signal Processing And Recognition) and MFCC(Mel frequency Cepstral Coefficients) features, to yield high recognition accuracy even in adverse noisy conditions. A new ranking scheme is also proposed in order to stabilize the rank of features in various noise levels by taking Arithmetic Mean of the F-Ratio scores obtained from various levels of Signal to Noise Ratio (SNR). The result is presented for a Text-Dependent ASR system with 20 speaker database. An RBF (Radial Basis Function) Neural Network is used for Recognition purpose. Also a comparative study has been performed for recognition accuracies of optimized MFCC and TESPAR features and we conclude that new proposed average F-Ratio technique has resulted in better accuracy compared to simple F-ratio in noisy environment and also we came to know that TESPAR features are more redundant compared to MFCC.
Design of Aperture Coupled Microstrip Antenna Using Radial Basis Function Networks  [PDF]
Tanushree Bose, Nisha Gupta
Wireless Engineering and Technology (WET) , 2010, DOI: 10.4236/wet.2010.12010
Abstract: This paper, two Artificial Neural Network (ANN) models using radial basis function (RBF) nets are developed for the design of Aperture Coupled Microstrip Antennas (ACMSA) for different number of design parameters. The effect of increasing the number of design parameters on the ANN model is also discussed in this work. The performances of the models when compared are found that on decreasing the number of design parameters, accuracy of the model is in-creased. The results given by the prepared models are comparable with the results of the IE3D software. So, these models are accurate enough to measure the design parameters of ACMSAs. Thus the neural network approach elimi-nates the long time consuming process of finding various designing parameters using costly software packages.
Multi-Deployment of Dispersed Power Sources Using RBF Neural Network  [PDF]
Yaser Soliman Qudaih, Takashi Hiyama
Energy and Power Engineering (EPE) , 2010, DOI: 10.4236/epe.2010.24032
Abstract: Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process; so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy; therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi- DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method.
Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed  [PDF]
Hadi Memarian, Siva Kumar Balasundram
Journal of Water Resource and Protection (JWARP) , 2012, DOI: 10.4236/jwarp.2012.410102
Abstract: Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.
The Research of Freight Volume Forecasting Based on Industrial Structure Development

熊强, 孙有望
Open Journal of Transportation Technologies (OJTT) , 2016, DOI: 10.12677/OJTT.2016.52004
As an important issue in the research of the relation between the transportation requirement and economy development, the freight volume forecasting is significant to the transportation planning and economy development. The social economy level was the internal determinant to the freight demand, so this paper tried to make freight volume forecasting from using industrial structure and economy development indicators, found forecasting model through RBF neural network and make positive analysis, then the model offered new method in freight volume forecasting work for its preferable forecasting ability and application value.
Atmospheric Environmental Quality Assessment RBF Model Based on the MATLAB  [PDF]
Zhonghua Fei, Dinggui Luo, Zhefei He, Bo Li
Journal of Environmental Protection (JEP) , 2012, DOI: 10.4236/jep.2012.37081
Abstract: A new method-RBF model is found to assess the atmospheric quality by use of the PREMNMX function in MATLAB to pretreat the original data and the RAND function to construct enough training samples, checking samples and outputs of their targets through linear interpolation between grades of the atmospheric quality evaluation standard. A favorable assessment result is achieved by applying this method to assess atmospheric environmental quality in a city, which shows this new method is meaningful in improving the precision and scientificity of atmospheric environmental quality assessment.
Application of Neural Network in Diagnosis of Diesel Engine

秦恺, 任开春, 牟浩, 吴珍毅, 田力
Artificial Intelligence and Robotics Research (AIRR) , 2013, DOI: 10.12677/AIRR.2013.21013
Intelligent fault diagnosis is the only way for the development of diagnostic techniques of modern equipment, but also the direction of development of the current diagnostic techniques. In this paper, basic principle of BP and RBF network using Matlab neural network toolbox, engine fault diagnosis based on BP network and RBF network were compared, and the diagnostic results of the two algorithms.
An Empirical Study On Fault Localization And Effective Test Case Selection By Neural Network
A. Pravin,Dr. S. Srinivasan
Indian Journal of Computer Science and Engineering , 2012,
Abstract: A Radial basis function (RBF) neural network based fault localization technique is proposed in this paper to assist programmers in locating bugs effectively. Here we employ a three-layered feed forward artificial neural network with a radial basis function for its hidden unit activation and for linear function with its output layeractivation. Here the neural network is trained to have a good relationship between the statement coverage information of a test case and its corresponding execution result to get a success or failure. The trained network is then given as an input to a set of virtual test cases, each covering a single statement, and the output of the network, for each virtual test case, is considered to be the suspiciousness of the corresponding covered statement. A statement with a higher suspiciousness has a higher likelihood of contain a bug, and thus, statement can be ranked in descending order of their suspiciousness. The Ranking can then be examined one by one, starting from the top, until a bug is located. Six case studies on different programs were conduced, with each faulty version contain a distinct bug, and the result clearly show that our proposed technique is much more effective than Tarantula, another popular fault localization technique.
Prototyping Neuroadaptive Smart Antenna for 3G Wireless Communications
To William,Salcic Zoran,Nguang Sing Kiong
EURASIP Journal on Advances in Signal Processing , 2005,
Abstract: This paper describes prototyping of a neuroadaptive smart antenna beamforming algorithm using hardware-software implemented RBF neural network and FPGA system-on-programmable-chip (SoPC) approach. The aim is to implement the adaptive beamforming unit in a combination of hardware and software by estimating its performance against the fixed real-time constraint based on IMT-2000 family of 3G cellular communication standards.
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