Redial Basis Function Research Articles

Radial basis function neural network with genetic algorithm for discrimination of recombination hotspots in saccharomyces cerevisiae

Recombination influences the evolution of saccharomyces cerevisiae. Genomic regions where recombinations occurs are known as recombination hotspots. There are two kind of hotspots for recombination. The spots where recombination occurs more frequently are called recombination hot spots and regions where recombination occurs less frequently are known as cold spots. In this work, we have formulated methods based on neural network models for the classification of these hot and cold recombination spots on the basis of compositional features of nucleotide sequences. These models were validated using tenfold cross validation technique. The classification accuracy of 83%, 82%, and 78% were achieved using radial basis function neural network with genetic algorithm, radial basis function neural network and multilayer perceptron models respectively. Moreover, the performance of these model were evaluated on different ct classification measurements. Furthermore, results indicate that redial basis function neural network with genetic algorithm gives best result.

Radial basis function neural network with genetic algorithm for discrimination of recombination hotspots in saccharomyces cerevisiae

Recombination influences the evolution of saccharomyces cerevisiae. Genomic regions where recombinations occurs are known as recombination hotspots. There are two kind of hotspots for recombination. The spots where recombination occurs more frequently are called recombination hot spots and regions where recombination occurs less frequently are known as cold spots. In this work, we have formulated methods based on neural network models for the classification of these hot and cold recombination spots on the basis of compositional features of nucleotide sequences. These models were validated using tenfold cross validation technique. The classification accuracy of 83%, 82%, and 78% were achieved using radial basis function neural network with genetic algorithm, radial basis function neural network and multilayer perceptron models respectively. Moreover, the performance of these model were evaluated on different ct classification measurements. Furthermore, results indicate that redial basis function neural network with genetic algorithm gives best result.

Modeling of reactive orange 16 dye removal from aqueous media by mesoporous silica/ crosslinked polymer hybrid using RBF, MLP and GMDH neural network models

In this study, SBA-15 mesoporous silica was synthesized and functionalized with cross-linked polyacrylic acid and used to remove Reactive Orange 16 (RO16) from aqueous media. X-ray diffraction (XRD), filed emission scanning electron microscope (FE-SEM), fourier transform infrared spectrometer (FT-IR), thermo gravimetry analysis (TGA) and BET method were used to study the characteristics of the resulting nanocomposite (SBA-15/CPAA). The effective parameters on dye removal, including pH, contact time, temperature and adsorbent dosage were investigated and optimized. The optimum condition was: pH = 2, adsorbent dosage of 0.03 g and contact time of 60 min at 25 °C. Uv–Vis spectrophotometer was used to determine the amount of residual dye in solution. Multilayer Perceptron (MLP), Redial Basis Function (RBF) and Group Method of Data Handling (GMDH) models have been applied for predicting adsorption value according to the amount of pH, dosage, temperature, concentration and contact time. Comparison of these models revealed that the MLP model was more accurate than the other two models.

Kinematic Synthesis of Parallel Manipulator via Neural Network Approach

In this research, Artificial Neural Networks (ANNs) have been used as a powerful tool to solve the inverse kinematic equations of a parallel robot. For this purpose, we have developed the kinematic equations of a Tricept parallel kinematic mechanism with two rotational and one translational degrees of freedom (DoF). Using the analytical method, the inverse kinematic equations are solved for specific trajectory, and used as inputs for the applied ANNs. The results of both applied networks (Multi-Layer Perceptron and Redial Basis Function) satisfied the required performance in solving complex inverse kinematics with proper accuracy and speed.

Artificial neural network application for predicting soil distribution coefficient of nickel

The distribution (or partition) coefficient (Kd) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict Kd values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting Kd of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The Kd values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and Kd of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of Kd. Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network.

Radar Emitter Signal Recognition Based on EMD and Neural Network

Radar emitter signal (RES) recognition is the important content in radar reconnaissance and signal processing. In order to study the problem of RES recognition, and to improve the RES recognition rate of the electronic warfare equipment, the empirical mode decomposition (EMD) theory and wavelet packet (WP) are introduced into RES feature extraction. A new RES recognition method is proposed based on WP, EMD and neural network (NN). It uses wavelet packet to finish decomposition, de-noising and reconstruction of the RES. Then obtain the intrinsic mode function (IMF) through EMD, which can embody the characteristics of the RES. The energy of each IMF are calculated and normalized, which would be regarded as the feature vector. By constructing back propagation neural network (BPNN) classifier and redial basis function neural network (RBFNN) classifier, it realizes the RES recognition finally. Experiment results show that the RES recognition method based on WP, EMD and NN is an effective recognition method, which can achieve satisfying correct recognition rate in a larger signal to noise ratio, and has certain reference value in follow-up in-depth study.

A comparison study on a chemical reactor modelling with a physical model and PLRBF networks

The paper presents the modelling practice and considerations for a multivariable process with a first-principle model and three redial basis function (RBF) networks. The process is a laboratory-scaled three-input three-output chemical reactor rig. The RBF networks used are standard RBF networks, pseudo-linear RBF (PLRBF) networks and adaptive PLRBF networks. The first-principle model, the network structures and training algorithms are briefly reviewed. Real data collection with the design of the excitation signal is described. The four models are evaluated by multi-step-ahead prediction errors and the comparison is made. The methods and considerations provide useful experience for deriving data-driven models for industrial processes.

Sensor fault diagnosis for automotive engines with real data evaluation

In this paper, a new fault diagnosis method using an adaptive neural network for automotive engines is developed. A redial basis function (RBF) network is used as a fault classifier with its widths and weights on-line adapted to cope with model uncertainty and time varying dynamics caused by mechanical wear of engine parts, environment change, etc. Five different sensors are investigated for an automotive engine including throttle angle, manifold pressure, manifold temperature, crankshaft speed and engine torque. The engine data is acquired from a one-litre Volkswagen petrol engine test bed under different operating states, and then simulated multiplicative faults are superimposed. The real data experiments confirm that sensor faults as small as 2% can be detected and isolated clearly. The developed scheme is capable of diagnosing faults in on-line mode and can be directly implemented in an on-board engine diagnosis system.