Radial Basis Function Artificial Neural Networks and Fuzzy Logic
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Identification of Nonlinear Systems Using Radial Basis Function Neural Network C
World Academy of Science, Engineering and Technology International Journal of Mechanical and Mechatronics Engineering Vol:8, No:9, 2014 Identification of Nonlinear Systems Using Radial Basis Function Neural Network C. Pislaru, A. Shebani parameters (centers, width and weights). Other researchers Abstract—This paper uses the radial basis function neural used the genetic algorithm to improve the RBFNN network (RBFNN) for system identification of nonlinear systems. performance; this will be a more complicated identification Five nonlinear systems are used to examine the activity of RBFNN in process [4]–[15]. system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this II. ARTIFICIAL NEURAL NETWORKS (ANNS) work for modelling the non-linear dynamic models, where the K- A. Simple Perceptron Means clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast The simple perceptron shown in Fig. 1, it is a single convergence and can handle large data sets. The least mean square processing unit with an input vector X = (x0,x1, x2…xn). This method is used to adjust the weights to the output layer, and vector has n elements and so is called n-dimensional vector. Euclidean distance method used to measure the width of the Gaussian Each element has its own weights usually represented by function. the n-dimensional weight vector, e.g. W = (w0,w1,w2...wn). Keywords—System identification, Nonlinear system, Neural networks, RBF neural network. -
Diagnosis of Brain Cancer Using Radial Basis Function Neural Network with Singular Value Decomposition Method
International Journal of Machine Learning and Computing, Vol. 9, No. 4, August 2019 Diagnosis of Brain Cancer Using Radial Basis Function Neural Network with Singular Value Decomposition Method Agus Maman Abadi, Dhoriva Urwatul Wustqa, and Nurhayadi observation and analysis them. Then, they consult the results Abstract—Brain cancer is one of the most dangerous cancers of the analysis to the doctor. The diagnosis using MRI image that can attack anyone, so early detection needs to be done so can only categorize the brain as normal or abnormal. that brain cancer can be treated quickly. The purpose of this Regarding the important of the early diagnosis of brain study is to develop a new procedure of modeling radial basis cancer, many researchers are tempted doing research in this function neural network (RBFNN) using singular value field. Several methods have been applied to classify brain decomposition (SVD) method and to apply the procedure to diagnose brain cancer. This study uses 114 brain Magnetic cancer, such as artificial neural networks based on brain MRI Resonance Images (MRI). The RBFNN model is constructed by data [3], [4], and backpropagation network techniques using steps as follows; image preprocessing, image extracting (BPNN) and principal component analysis (PCA) [5], using Gray Level Co-Occurrence Matrix (GLCM), determining probabilistic neural network method [6], [7]. Several of parameters of radial basis function and determining of researches have proposed the combining several intelligent weights of neural network. The input variables are 14 features system methods, those are adaptive neuro-fuzzy inference of image extractions and the output variable is a classification of system (ANFIS), neural Elman network (Elman NN), brain cancer. -
Radial Basis Function Network
Radial basis function network In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control. They were first formulated in a 1988 paper by Broomhead and Lowe, both researchers at the Royal Signals and Radar Establishment.[1][2][3] Contents Network architecture Normalized Normalized architecture Theoretical motivation for normalization Local linear models Training Interpolation Function approximation Training the basis function centers Pseudoinverse solution for the linear weights Gradient descent training of the linear weights Projection operator training of the linear weights Examples Logistic map Function approximation Unnormalized radial basis functions Normalized radial basis functions Time series prediction Control of a chaotic time series See also References Further reading Network architecture Radial basis function (RBF) networks typically have three layers: an input layer, a hidden layer with a non- linear RBF activation function and a linear output layer. The input can be modeled as a vector of real numbers . The output of the network is then a scalar function of the input vector, , and is given by where is the number of neurons in the hidden layer, is the center vector for neuron , and is the weight of neuron in the linear output neuron. Functions that depend only on the distance from a center vector are radially symmetric about that vector, hence the name radial basis function. -
RBF Neural Network Based on K-Means Algorithm with Density
RBF Neural Network Based on K-means Algorithm with Density Parameter and Its Application to the Rainfall Forecasting Zhenxiang Xing, Hao Guo, Shuhua Dong, Qiang Fu, Jing Li To cite this version: Zhenxiang Xing, Hao Guo, Shuhua Dong, Qiang Fu, Jing Li. RBF Neural Network Based on K-means Algorithm with Density Parameter and Its Application to the Rainfall Forecasting. 8th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2014, Beijing, China. pp.218-225, 10.1007/978-3-319-19620-6_27. hal-01420235 HAL Id: hal-01420235 https://hal.inria.fr/hal-01420235 Submitted on 20 Dec 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License RBF Neural Network Based on K-means Algorithm with Density Parameter and its Application to the Rainfall Forecasting 1,2,3,a 1,b 4,c 1,2,3,d 1,e Zhenxiang Xing , Hao Guo , Shuhua Dong , Qiang Fu , Jing Li 1 College of Water Conservancy &Civil Engineering, Northeast Agricultural University, Harbin 2 150030, China; Collaborative Innovation Center of Grain Production Capacity Improvement 3 in Heilongjiang Province, Harbin 150030, China; The Key lab of Agricultural Water resources higher-efficient utilization of Ministry of Agriculture of PRC, Harbin 150030,China; 4 Heilongjiang Province Hydrology Bureau, Harbin, 150001 a [email protected], [email protected], [email protected], [email protected], e [email protected] Abstract. -
Ensemble of Radial Basis Neural Networks with K-Means Clustering for Radiša Ž
Ensemble of Radial Basis Neural Networks With K-means Clustering for Radiša Ž. Jovanovi ć Assistant Professor Heating Energy Consumption Prediction University of Belgrade Faculty of Mechanical Engineering Department of Automatic Control For the prediction of heating energy consumption of university campus, neural network ensemble is proposed. Actual measured data are used for Aleksandra A. Sretenovi ć training and testing the models. Improvement of the predicton accuracy Teaching Assistant using k-means clustering for creating subsets used to train individual University of Belgrade Faculty of Mechanical Engineering radial basis function neural networks is examined. Number of clusters is Department of Thermal Science varying from 2 to 5. The outputs of ensemble members are aggregated using simple, weighted and median based averaging. It is shown that ensembles achieve better prediction results than the individual network. Keywords: heating consumption prediction, radial basis neural networks, ensemble 1. INTRODUCTION description of the relationship between the independent variables and the dependent one. The data-driven The study of the building energy demand has become a approach is useful when the building (or a system) is topic of great importance, because of the significant already built, and actual consumption (or performance) increase of interest in energy sustainability, especially data are measured and available. For this approach, after the emanation of the EPB European Directive. In different statistical methods can be used. Europe, buildings account for 40% of total energy use Artificial neural networks (ANN) are the most used and 36% of total CO 2 emission [1]. According to [2] artificial intelligence models for different types of 66% of the total energy consumption of residential prediction. -
Radial Basis Function Neural Networks: a Topical State-Of-The-Art Survey
Open Comput. Sci. 2016; 6:33–63 Review Article Open Access Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri*, and Sung-Bae Cho Radial basis function neural networks: a topical state-of-the-art survey DOI 10.1515/comp-2016-0005 puts and bias term are passed to activation level through a transfer function to produce the output (i.e., p(~x) = Received October 12, 2014; accepted August 24, 2015 PD ~ fs w0 + i=1 wi xi , where x is an input vector of dimen- Abstract: Radial basis function networks (RBFNs) have sion D, wi , i = 1, 2, ... , D are weights, w0 is the bias gained widespread appeal amongst researchers and have 1 weight, and fs(o) = 1+e−ao , a is the slope) and the units are shown good performance in a variety of application do- arranged in a layered feed-forward topology called Feed mains. They have potential for hybridization and demon- Forward Neural Network [2]. The network is then trained strate some interesting emergent behaviors. This paper by back-propagation learning scheme. An MLP with back- aims to oer a compendious and sensible survey on RBF propagation learning is also known as Back-Propagation networks. The advantages they oer, such as fast training Neural Networks (BPNNs). In BPNNs a common barrier is and global approximation capability with local responses, the training speed (i.e., the training speed increases as the are attracting many researchers to use them in diversied number of layers, and number of neurons in a layer grow) elds. The overall algorithmic development of RBF net- [3].