Radial Basis Function Network Algorithm Research Articles

Adaptive sliding mode attitude control of quaternion model for aircraft based on neural network minimum parameter learning method

AbstractThis paper studied the back-stepping adaptive sliding mode control (SMC) attitude problem of quaternion aircraft model based on radial basis function (RBF) network approximation. Firstly, a sliding mode controller is designed based on the back-stepping method (BSM) for the nonlinear aircraft model. Secondly, a RBF network algorithm is designed to compensate for the unknown and uncertain parts of the aircraft system. RBF network has simple network structure and good generalisation ability, avoids lengthy and unnecessary calculations, realises adaptive approximation of unknown parts in the aircraft model, and through the adjustment of adaptive weights, the convergence and stability of the entire closed-loop system (CLS) are guaranteed. Finally, the anti-interference performance of the controller is verified by simulation of the actuator fault model. Our proposed method has all-right control performance indicated by the simulation results.

PV-Fed Micro-Inverter with Battery Storage for Single Phase Grid Applications

Nowadays, micro-inverters are trending due to the latest features consisting in PV technology. However, integration of a high-gain boost converter is needed to improve the low rating output voltage of PV modules to meet the load demand. A high-gain converter with less component count is required for grid integration systems. This article proposes a new quasi z-source based high-gain DC–DC converter with reduced components, cost, and size. The proposed converter is integrated with the micro-inverter for single-phase grid applications along with battery storage. A radial basis function network algorithm is utilized for a PV system to track the maximum power point, an efficient hysteresis current controller is applied for the full-bridge inverter for the robust regulation of current and the energy can be stored through the bidirectional dc/dc converter to supply the power to grid when the sources are drained conditions. An efficient PV extraction, low total harmonic distortion and achieves unity power factor which is perfectly suitable for grid-connected PV systems. The operating modes in every aspect and analysis of the proposed configuration have been investigated in detail. The implementation of control strategies is discussed in detail. Simulation of the proposed micro-inverter is designed in MATLAB/SIMULINK tool to validate the efficiency under variable operating conditions. With the successful implementation of the proposed technique, total harmonic distortion is reduced to 2.05% and efficiency is enhance to the 98%.

An online self-adaptive RBF network algorithm based on the Levenberg-Marquardt algorithm

ABSTRACT Aiming at the problem that the Levenberg-Marquardt (LM) algorithm can not train online radial basis function (RBF) neural network and the deficiency in the RBF network structure design methods, this paper proposes an online self-adaptive algorithm for constructing RBF neural network (OSA-RBFNN) based on LM algorithm. Thus, the ideas of the sliding window method and online structure optimization methods are adopted to solve the proposed problems. On the one hand, the sliding window method enables the RBF network to be trained online by the LM algorithm making the RBF network more robust to the changes in the learning parameters and faster convergence compared with the other investigated algorithms. On the other hand, online structure optimization can adjust the structure of the RBF network based on the information of training errors and hidden nodes to track the non-linear time-varying systems, which helps to maintain a compact network and satisfactory generalization ability. Finally, verified by simulation analysis, it is demonstrated that OSA-RBFNN exhibits a compact RBF network.

A distributed cooperative learning algorithm based on Zero-Gradient-Sum strategy using Radial Basis Function Network

Abstract This paper aims to propose a distributed cooperative learning (DCL) algorithm for Radial Basis Function Network (RBFN), where training samples are often extremely large-scale, high-dimensional and located on distributed nodes over undirected and connected networks. The algorithm is investigated for RBFN with different basic functions, such as Gaussian function, multi-quadric function, thin plate spline function and so on. The proposed DCL–RBFN algorithm is based on Zero-Gradient-Sum (ZGS) distributed optimization strategy and works in a fully distributed fashion, which has no requirement on central node and communication of raw data from each node during the training process. Moreover, in order to avoid the waste of unnecessary network resources, an event-triggered communication mechanism is applied to our algorithm. Each node exchanges learned information with its neighboring nodes using the ZGS strategy under event-triggered mechanism. The convergence of the proposed DCL–RBFN and event-triggered DCL–RBFN algorithms are presented by using the Lyapunov method. It is proved that the proposed algorithms have exponential convergence speed and require less communication data over networks. Finally, a series of numerical simulations are given to verify the advantages of the proposed algorithms.

Adaptive neural network sliding mode control of shipboard container cranes considering actuator backlash

Abstract Offshore container crane is a highly under-actuated nonlinear system whereas only two control inputs are employed for driving six system outputs. Controlling such a system is not easy since it faces with many challenges composed of actuator backlash, geometrical nonlinearities, seawater viscoelasticity, cable flexibility, strong wave and wind disturbances, and considerable lack of actuators. This article proposes a robust adaptive system for a ship-mounted container crane with the disadvantages mentioned above. The controller structure is constructed using second-order sliding mode control (SOSMC), and a modeling estimator is designed on the basis of radial basis function network (RBFN). While other adaptive control techniques only estimates system parameters, the adaptive RBFN algorithm approximates almost all the structure of a crane model, including system parameters. Simulations and experiments are conducted to verify the superiority of the proposed control system.

Implementation of Elbow Method to improve the Gases Classification Performance based on the RBFN-NSG Algorithm

Currently, the radial basis function network (RBFN) and various other neural networks are employed to classify gases using chemical sensors arrays, and their performance is steadily improving. In particular, the identification performance of the RBFN algorithm is being improved by optimizing parameters such as the center, width, and weight, and improved algorithms such as the radial basis function network-stochastic gradient (RBFN-SG) and radial basis function network-normalized stochastic gradient (RBFN-NSG) have been announced. In this study, we optimized the number of centers, which is one of the parameters of the RBFN-NSG algorithm, and observed the change in the identification performance. For the experiment, repeated measurement data of 8 samples were used, and the elbow method was applied to determine the optimal number of centers for each sample of input data. The experiment was carried out in two cases(the only one center per sample and the optimal number of centers obtained by elbow method), and the experimental results were compared using the mean square error (MSE). From the results of the experiments, we observed that the case having an optimal number of centers, obtained using the elbow method, showed a better identification performance than that without any optimization.

Direct optimization of inference model for human activity and posture class recognition

The demand for ambulatory monitoring is rising due to the widespread adoption of home-based healthcare models for the increasing elderly population. Since monitoring patients makes it possible to provide remote medical services based on real-world contexts, context-aware technology is regarded as a crucial element in core module development for ubiquitous healthcare solutions. This paper describes the design and implementation of a class label tagging system for human activity recognition based on sensor data from a single tri-axial accelerometer attached to the waist of a human subject. Our human activity and posture classifier (APC) was designed to model more effectively an event that takes place over a period of time. Consequently, the APC problem becomes the process of tagging class labels onto sequential data from a single tri-axial accelerometer. This sequential tagging naturally led to our hypothesis that a linear combination of the classification model and the prior, approximated by observing a state transition event of human activity and posture (AP), may decrease the error rate of the system. Therefore, this paper aims at testing the aforementioned experimental hypothesis while explicating our method of direct optimization, also known as direct loss minimization. Our experimental hypothesis is based on the view that the task of labeling a series of class tags on a data object of the AP event should be supported by a statistical model capable of robustly capturing the probabilistic context influencing the generation of a set of features at a given discrete time. A radial basis function network algorithm was used for parameter estimation of a class likelihood inference model augmented by training a state transition model to capture the sequential nature of a posture or activity generation event. The linear combination requires our design step to have a further optimization, in addition to learning the parameters of each inference model, of the model parameters that represent the relative contribution to the decision process. Previous studies have elaborated on an effective approach to direct optimization of model parameters when an appropriate evaluation metric exists for a given problem domain. In our case, f-measure was used as an objective function of the direct optimization. We used local beam search to optimize directly the weight vector for the likelihood and the prior. Our method is tested on the set of nine-dimensional feature vectors contained in 69,376 lines of sequential AP data. The experiment shows that embedding prior probability into the recognition process decreases the error rate in a set of categories. Several experiments proved the efficiency of our proposed method and the usefulness of the system.

Locally regularised two-stage learning algorithm for RBF network centre selection

Nonlinear system models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually application dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsimonious network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise. Specifically, each output layer weight is assigned a hyperparameter, a large value of such a parameter forcing the associated output layer weight to be near to zero. Sparsity is achieved by removing irrelevant RBF centres from the network. The efficacy of proposed algorithm from the original two-stage construction method is retained. Numerical analysis shows that this new method only needs about half of the computation involved in the locally regularised orthogonal least squares (LROLS) alternative. Results from two simulation examples are presented to show that the nonlinear system models resulting from this new approach are superior in terms of both sparsity and generalisation capability.

Research on Suspension System Based on Radial-Basis Function Algorithm

In this paper, a four degrees of freedom half body vehicle suspension system is developed .The Radial-Basis Function network algorithm is used to control the suspension system. With the aid of software Matlab/Simulink , the simulation model is achieved. A lot of simulation work is done. Simulation results demonstrate that the proposed active suspension system proves to be effective in vibration reduction of the suspension system.

Two-stage RBF network construction based on particle swarm optimization

The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.

Research on Active Suspension System Based on BP and RBF Network Algorithm

A six degrees of freedom half body vehicle suspension system is presented in the paper .The Back Propagation neural network algorithm and the Radial-Basis Function network algorithm is adopted to control the suspension system. With the aid of software Matlab/Simulink , the simulation model is obtained. A great deal of simulation work is done. Simulation results demonstrate that both the designed radius basis function neural network and the back propagation neural network work well for the proposed vehicle suspension model in the paper .

An Improved Fast Training Algorithm for RBF Networks Using Symmetry-Based Fuzzy C-Means Clustering

In fuzzy C-means (FCM) clustering, each data point belongs to a cluster to a degree specified by a membership grade. FCM partitions a collection of vectors in c fuzzy groups and finds a cluster center in each group such that the dissimilarity measure is minimized. This paper presents a training algorithm for the radial basis function (RBF) network using symmetry-based Fuzzy C-means (SFCM) clustering method which is the modified version of FCM clustering method based on point symmetry distance measure. The training algorithm which uses SFCM clustering method to train the network has a number of advantages such as faster training time, more accurate predictions and reduced network architecture compared to the standard RBF networks. The proposed training algorithm has been implemented in the RBF networks created by the newrb function of MATLAB which uses gradient based iterative method as learning strategy, therefore the new network will undergo a hybrid learning process. The networks called Symmetry-based Fuzzy C-means Clustering–Radial Basis Function Network (SFCM/RBF) has been tested against the standard RBF network and the networks called standard Fuzzy C-means Clustering (FCM)-RBF network (FCM/RBF) in forecasting. The experimental models has been tested on three real world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, and Phytoplankton problem. Keywords: Fuzzy c-means clustering; SFCM; Radial basis function network; point symmetry distance; forecasting.

A hybrid MPSO-BP structure adaptive algorithm for RBFNs

This paper introduces a novel hybrid algorithm to determine the parameters of radial basis function neural networks (number of neurons, centers, width and weights) automatically. The hybrid algorithm combines the mix encoding particle swarm optimization algorithm with the back propagation (BP) algorithm to form a hybrid learning algorithm (MPSO-BP) for training Radial Basis Function Networks (RBFNs), which adapts to the network structure and updates its weights by choosing a special fitness function. The proposed method is used to deal with three nonlinear problems, and the results obtained are compared with existent bibliography, showing an improvement over the published methods.

INTEGRATIVE COMPUTER-AIDED DIAGNOSTIC WITH BREAST THERMOGRAM

Thermography is a non-invasive and non-contact imaging technique widely used in the medical arena. This paper investigates the analysis of thermograms with the use of biostatistical methods and Artificial Neural Networks (ANN). It is desired that through these approaches, highly accurate diagnosis using thermography techniques can be established. The proposed advanced technique is a multipronged approach comprising of Linear Regression (LR), Radial Basis Function Network (RBFN) and Receiver Operating Characteristics (ROC). It is a novel and integrative technique that can be used to analyze complicated and large numerical data. In this study, the advanced technique will be used to analyze breast cancer thermogram for diagnosis purposes. The use of LR shows the correlation between the variables and the actual health status (healthy or cancerous) of the subject, which is decided by using mammography. This is important when selecting the variables to be used as inputs, in particular, for building the neural network. For ANN, RBFN is applied. Based on the various inputs fed into the network, RBFN will be trained to produce the desired outcome, which is either positive for cancerous or negative for healthy cases. When this is done, the RBFN algorithm will possess the ability to predict the outcome when there are new input variables. The advantages of using RBFN include fast training, superior classification and decision making abilities as compared to other networks such as back-propagation. Next, ROC is used to evaluate the accuracy, sensitivity and specificity of the outcome of RBFN Test files. The best results obtained are an accuracy (score) rate of 80.95%, with 81.2% sensitivity and 88.2% specificity. For breast cancer diagnosis, clinical examination by experienced doctors has an accuracy rate of approximately 60–70%. Hence, the proposed method has a higher accuracy rate than the existing practice. Through the use of Bio-statistical methods and ANN, improvements are made in thermography application with regard to achieving a higher level of accuracy rate in diagnosis as compared to clinical examination. It has now become possible to use thermography as a powerful adjunct tool for breast cancer detection, together with mammography for diagnosis purposes.

Normalization approach to the stochastic gradient radial basis function network algorithm for odor sensing systems

A method of adapting centers and weights in the radial basis function network (RBFN) is introduced using a normalization method to the stochastic gradient (RBFN-SG) algorithm for odor classification. The RBFN input data vector is from a conducting polymer sensor array. Using Taylor's expansion, a normalized form of the RBFN-SG algorithm is derived. The tracking dynamics of the normalized method appear to be less sensitive to widely varying inputs than the RBFN-SG. Experimental results of the proposed method have shown a faster learning speed, a lower mean squared error (MSE) and better classification performance.

A Comparative Study for Modeling of Hot-Rolled Steel Plate Classification Using a Statistical Approach and Neural-Net Systems

Classification of flat steel products from the point of view of reaching the target property is a common practice in industries. In most classification problems, standard statistical methods generally place constraints such as continuous, differentiable, otherwise well behaved, etc. However, Artificial Neural Networks (ANN) has an ability to learn and generalize any complex system without making any model assumptions. This work emphasizes on making performance evaluation of usual statistical techniques such as general clustering like K-means, partition around medoid (PAM), classification and regression tree (CART), linear discriminant analysis (LDA) vis-a-vis usage of multilayer perceptron (MLP) learning algorithm, radial basis function (RBF) family of methods and Kohonen networks. To recommend the utility of modeling, some real-life industrial databases are used. It can be observed from the results that learning of networks through back-propagation yielded minimum misclassification of two groups of heats including minimization of train-test error. The statistical techniques such as LDA and CART provide the same results of misclassification along with the results obtained from perceptron learning, RBF network algorithm and Kohonen learning with learning-vector quantization (LVQ) algorithm.

A NOVEL MODULAR NONLINEAR NETWORK FOR FAULT DIAGNOSIS AND SUPERVISED PATTERN CLASSIFICATION

A novel modular network is proposed in this work for supervised pattern classification. The parameters of the hidden layer are determined using polygonal line algorithm. No further training of the network is required. Firstly, an abnormality is detected and responsible sensors identified using polygonal line based radial basis function network algorithm. Furthermore, the proposed strategy is applied for fault diagnosis. A continuous pilot plant is selected as the case study to show the efficiency of the proposed strategy. The result shows that, the proposed framework is a promising direction towards fault detection and diagnosis in real time, non-linear systems.

A new EM-based training algorithm for RBF networks

In this paper, we propose a new Expectation–Maximization (EM) algorithm which speeds up the training of feedforward networks with local activation functions such as the Radial Basis Function (RBF) network. In previously proposed approaches, at each E-step the residual is decomposed equally among the units or proportionally to the weights of the output layer. However, these approaches tend to slow down the training of networks with local activation units. To overcome this drawback in this paper we use a new E-step which applies a soft decomposition of the residual among the units. In particular, the decoupling variables are estimated as the posterior probability of a component given an input–output pattern. This adaptive decomposition takes into account the local nature of the activation function and, by allowing the RBF units to focus on different subregions of the input space, the convergence is improved. The proposed EM training algorithm has been applied to the nonlinear modeling of a MESFET transistor.

Analysis of minimal radial basis function network algorithm for real-time identification of nonlinear dynamic systems

A performance analysis is presented of the minimal resource allocating network (MRAN) algorithm for online identification of nonlinear dynamic systems. Using nonlinear time-invariant and time-varying identification benchmark problems, MRAN's performance is compared with the online structural adaptive hybrid learning (ONSAHL) algorithm. Results indicate that the MRAN algorithm realises networks using fewer hidden neurons than the ONSAHL algorithm, with better approximation accuracy. Methods for improving the run-time performance of MRAN for real-time identification of nonlinear systems are developed. An extension to MRAN is presented, which utilises a winner neuron strategy and is referred to as the extended minimum resource allocating network (EMRAN). This modification reduces the computation load for MRAN and leads to considerable reduction in the identification time, with only a small increase in the approximation error. Using the same benchmark problems, results show that EMRAN is well suited for fast online identification of nonlinear plants.

Object classification in 3-D images using alpha-trimmed mean radial basis function network

We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics. The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a radial basis function (RBF) network and they are found by means of unsupervised training. A new robust training algorithm for RBF networks based on alpha-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing a spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images.