An LS-SVM classifier based methodology for avoiding unwanted responses in processes under uncertainties

Aimed at the non-stationary characteristics of rolling bearing vibration signal,a fault diagnosis method was proposed based on local-wave method and KPCA(kernel principal component analysis)-LSSVM(least squares support vector machine).Firstly,local wave decomposition was used to decompose rolling bearing vibration signal into several intrinsic mode function(IMF),whose feature energy,singular values and AR model parameters were computed as initial feature vectors.Secondly,ini tial feature vectors were mapped into a higher-dimensional space with KPCA,and the kemel principal components were extracted as new feature vectors,which used as the input of LSSVM for fault classification.The experimental results show the KPCA-LSSVM method improves LSSVM's classification performance by KPCA obtaining additional discriminative information,and has better generalization than direct LSSVM method,and can identify rolling bearing fault patterns more accurately.

Least squares support vector machine (LS-SVM) is a modified version of traditional support vector machine (SVM). LS-SVM considers equality constraints, therefore it solves a set of linear equations instead of quadratic programming problem in SVM. However, the sparseness of LS-SVM is lost due to itpsilas isin-sensitive cost function. Sparseness can be obtained by applying a pruning method, which eliminates some vectors with smallest support values and retrains the remaining samples. But iterative retraining is a time-consuming process. Motivated by the fact that boundary samples are more significant for constructing a LS-SVM classifier, this paper proposes a method of using pulse coupled neural networks (PCNNs) to search boundary samples of original data sets. The original data sets are mapped into some PCNN neurons, and a firing algorithm is designed to determine which samples lie at boundary region. It gives a novel approach to impose sparsity for LS-SVM. Experiments show that the proposed method can effectively detect boundary samples and speed up LS-SVM classifiers.

In this paper, a new fault diagnosis method for closed loop nonlinear system is proposed based on generalized frequency response functions(GFRFs) and least square support vector machine(LSSVM). A closed loop estimation method is used to identify GFRFs of controlled plant under closed loop condition. The kernel principal component analysis(KPCA) method with mixed kernel function is used to compress extract nonlinear spectrum feature. After obtained nonlinear spectrum feature, the LSSVM classifier is constructed for fault recognition. A simulation example about fault diagnosis of a nonlinear closed loop system is provided to illustrate the effectiveness of the proposed method. The results indicate that the proposed method has high accuracy, which can meet the requirements of fault diagnosis.

This study presents a multi-class least square support vector machines (LS-SVM)-based classifier for transformer fault diagnosis. First, the original binary classifier is extended for multi-class classification that is common in fault diagnosis by using combination schemes, that is, the minimal output coding, error correcting output codes, one-against-one and one-against-all schemes. Second, the algorithm of particle swarm optimisation is implemented to select the optimal feature parameters for the multi-class LS-SVM classifiers. Then the effectiveness of the proposed approach is verified on the basis of the experiments on benchmark classification data and real-world transformer data. For comparison purpose, three widely used transformer diagnosis methods such as the IEC criteria, back propagation neural network and standard support vector machines are utilised. The results show the proposed approach has a better performance both in training and testing accuracies.

Accurate electricity price prediction is key to the orderly operation of the electricity market. However, the uncertain, stochastic and fluctuant characteristics of electricity pricees make prediction difficult. With the aim of solving this issue, this investigation proposed a multi-stage intelligent model integrating the Beveridge–Nelson decomposition (B-N-D) model, the least square support vector machine (LSSVM), and a nature-inspired optimization model named the whale optimization algorithm (WOA). Firstly, the B-N-D model was utilized to decompose the hourly electricity price time series into determinacy component, periodic trend, and stochastic item. Secondly, the WOA–LSSVM model was proposed to forecast the future hourly data of three components respectively, of which the optimal parameters of LSSVM were determined by using WOA. Finally, the future hourly electricity price data were computed by multiplying the forecasted data of those terms. To verify the validity of the proposed electricity price prediction model in this paper, five comparison approaches based on the B-N-D approach were selected, which are auto-regressive integrated moving average (ARIMA), single LSSVM, LSSVM optimized by the fruit-fly optimization algorithm (FOA), LSSVM optimized by particle swarm optimization (PSO) models, and WOA–LSSVM without B-N-D. By comparatively analyzing the error criteria values of the above models through testing on the objective data of the Pennsylvania–New Jersey–Maryland (PJM) electricity market collected from 11 December 2017 to 18 December 2017, from 15 January 2018 to 22 January 2018, and from 1 February 2018 to 25 February 2018, we conclude that the constructed intelligent model in this paper can greatly enhance the prediction precision of electricity prices.

A normal least squares support vector machine (NLS-SVM) and its learning algorithm

Stock return forecast has been an important issue and difficult task for both shareholders and financial professionals. To tackle this problem, we introduce Least Square Support Vector Machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and Dynamic Inertia Weight Particle Swarm Optimization (W-PSO), that outperform standard PSO in parameter selection. The work of this paper is as following: First, forecast daily stock Return of Shanghai Security Exchanges of China using Back Propagation Neural Network (BPNN) and LS-SVM. Secondly, forecast the stock return using LS-SVM optimized by W- PSO. Finally, make a comparative analysis of the three algorithms. We reached conclusion that, in terms of forecast accuracy, LS-SVM outperforms BPNN, and when LS-SVM is optimized by W-PSO, the best result is achieved.

In nuclear power plant's peripheral environmental radiation monitoring wireless sensor network, the γ dose rate data collected in sensor node may be missed due to various factors, which will influence the validity of environmental radiation monitoring. To solve the problem, a missing data imputation algorithm based on least squares support vector machine(LSSVM) is proposed and particle swarm optimization is adopted to search the optimal set of the LSSVM model parameters. This LSSVM model imputes missing data utilizing node's previous monitoring data and neighbor node's current monitoring data jointly. The experimental results using the real radiation monitoring data around a nuclear power plant demonstrate that the proposed algorithm can achieve higher imputation accuracy than neural network model as well as direct LSSVM model, which has the non-optimized parameters.

Regularization parameter and kernel parameters play a crucial role on the accuracy and rapidity of the least squares support vector machine (LS-SVM) algorithm. Existing methods of tuning the parameters for LS-SVM have some disadvantages, such as difficulty of getting global optimum and poor convergent speed. In this work, we develop the parameter design of LS-SVM using quantum particle swarm optimization (QPSO) and apply the designed LS-SVM into node localization. According to the initial values, the optimal parameter values are obtained using the leave-10-out cross validation method. Simulation results show that the proposed method is compared to the coupling simulated annealing (CSA) LS-SVM and the improved particle swarm optimization (IPSO) LS-SVM.

This article adopts three soft computing techniques including support vector machine (SVM), least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.

In order to estimate the quality parameters in industrial processes, taking actual dynamic transition characteristics into account, a dynamic soft sensor modeling approach based on multiple least squares support vector machines (LSSVM) is presented. The input variables acquired in transition period are segmented according to their acquisition time point. Then LSSVMs are employed to map the relations of different time series of input variables to output variables. Moreover, a synthetical LSSVM is delivered to embody the dynamic characteristic of all the sub-networks to the model. A simulation example is put forward at last. The result shows that proposed method has simple dynamic structure and clear physical meanings, which features in better estimation precision and robustness than static soft sensors.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

Time series analysis is an important and complex problem in machine learning. In this paper, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome some shortcoming in the multilayer perceptron (MLP) and the PSO is used to tune the LS-SVM parameters automatically. A benchmark problem, Hénon map time series, has been used as an example for demonstration. It is showed this approach can escape from the blindness of man-made choice of the LS-SVM parameters. It enhances the efficiency and the capability of prediction.

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.