Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

The use of beamforming for efficient transmission has already been successfully implemented in practical systems and is absolutely necessary to even further increase spectral and energy efficiencies in some configurations of the next-generation wireless systems and for low earth orbit satellites. A remarkable capacity increase is then achieved and spectral congestion is minimized. In this context, this article proposes a novel complex multiple-input multiple-output radial basis function neural network (CMM-RBF) for transmitter beamforming, based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed CMM-RBF is compared with the least mean square (LMS) algorithm for beamforming with six dipoles arranged in a uniform and circular array and with 16 dipoles in a 2D-grid array. Simulation results show that the proposed solution presents lower steady-state mean squared error, faster convergence rate and enhanced half-power beamwidth (HPBW) when compared with the LMS algorithm in a nonlinear scenario.

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Development of combined back propagation algorithm and radial basis function for diagnosing depression patients

International Journal of Engineering & Technology ◽

10.14419/ijet.v4i1.4201 ◽

2015 ◽

Vol 4 (1) ◽

pp. 244

Author(s):

Bhuvana R. ◽

Purushothaman S. ◽

Rajeswari R. ◽

Balaji R.G.

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Mean Squared Error ◽

Back Propagation ◽

Optimum Number ◽

Back Propagation Algorithm ◽

Efficient System ◽

Propagation Algorithm ◽

Radial Basis

Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.

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Product Identification Using Image Processing and Radial Basis Function Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.761.120 ◽

2015 ◽

Vol 761 ◽

pp. 120-124

Author(s):

K.A.A. Aziz ◽

Abdul Kadir ◽

Rostam Affendi Hamzah ◽

Amat Amir Basari

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Radial Basis Function ◽

Basis Function ◽

Network Architecture ◽

Rbf Neural Networks ◽

Specific Product ◽

Product Identification ◽

Radial Basis

This paper presents a product identification using image processing and radial basis function neural networks. The system identified a specific product based on the shape of the product. An image processing had been applied to the acquired image and the product was recognized using the Radial Basis Function Neural Network (RBFNN). The RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using a fast two-stage training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and the spread of RBF. In this paper, fixed spread value was used for every cluster. The system can detect all the four products with 100% successful rate using ±0.2 tolerance.

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A Comparison between Multi-Layer Perceptron and Radial Basis Function Networks in Detecting Humans Based on Object Shape

Ibn AL- Haitham Journal For Pure and Applied Science ◽

10.30526/31.2.1950 ◽

2018 ◽

Vol 31 (2) ◽

pp. 210

Author(s):

Laith Jasim Saud ◽

Zainab Kudair Abass

Keyword(s):

Neural Network ◽

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Mean Squared Error ◽

Rbf Neural Network ◽

Human Detection ◽

Geometric Information ◽

Radial Basis ◽

Mlp Model

Human detection represents a main problem of interest when using video based monitoring. In this paper, artificial neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF) are used to detect humans among different objects in a sequence of frames (images) using classification approach. The classification used is based on the shape of the object instead of depending on the contents of the frame. Initially, background subtraction is depended to extract objects of interest from the frame, then statistical and geometric information are obtained from vertical and horizontal projections of the objects that are detected to stand for the shape of the object. Next to this step, two types of neural networks are used to classify the extracted objects. Tests have been performed on a sequence of frames, and the simulation results by MATLAB showed that the RBF neural network gave a better performance compared with the MLP neural network where the RBF model gave a mean squared error (MSE) equals to 2.36811e-18 against MSE equals to 2.6937e-11 achieved by the MLP model. The more important thing observed is that the RBF approach required less time to classify the detected object as human compared to the MLP, where the RBF took approximately 86.2% lesser time to give the decision.

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Radial basis function neural network for 2 satisfiability programming

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i1.pp459-469 ◽

2020 ◽

Vol 18 (1) ◽

pp. 459 ◽

Cited By ~ 3

Author(s):

Shehab Alzaeemi ◽

Mohd. Asyraf Mansor ◽

Mohd Shareduwan Mohd Kasihmuddin ◽

Saratha Sathasivam ◽

Mustafa Mamat

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Mean Squared Error ◽

Absolute Error ◽

Percentage Error ◽

Squared Error ◽

Training Technique ◽

Radial Basis ◽

Hidden Neurons

<span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.</span>

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Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate

Complexity ◽

10.1155/2018/6950124 ◽

2018 ◽

Vol 2018 ◽

pp. 1-19 ◽

Cited By ~ 3

Author(s):

Tiantian Luan ◽

Mingxiao Sun ◽

Guoqing Xia ◽

Daidai Chen

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Rbf Neural Network ◽

Variable Structure ◽

Learning Rate ◽

Rbf Neural Networks ◽

Fast Learning ◽

Radial Basis ◽

Hidden Layer

The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF.

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