Single Least Squares Support Vector Machine Research Articles

Slope stability prediction based on adaptive CE factor quantum behaved particle swarm optimization-least-square support vector machine

Since the prediction of slope stability is affected by the combination of geological and engineering factors with uncertainties such as randomness, vagueness and variability, the traditional qualitative and quantitative analysis cannot match the recent requirements to judge them accurately. In this study, we expect that the adaptive CE factor quantum behaved particle swarm optimization (ACE-QPSO) and least-square support vector machine (LSSVM) can improve the prediction accuracy of slope stability. To ensure the global search capability of the algorithm, we introduced three classical benchmark functions to test the performance of ACE-QPSO, quantum behaved particle swarm optimization (QPSO), and the adaptive dynamic inertia weight particle swarm optimization (IPSO). The results show that the ACE-QPSO algorithm has a better global search capability. In order to evaluate the stability of the slope, we followed the actual project and research literature and selected the unit weight, slope angle, height, internal cohesion, internal friction angle and pore water pressure as the main indicators. To determine whether the algorithm is scientifically and practically feasible for slope deformation prediction, the ACE-QPSO-, QPSO-, IPSO-LSSVM and single least-square support vector machine algorithms were trained and tested based on a real case of slope project with six index factors as the input layer of the LSSVM model and the safety factor as the output layer of the model. The results show that the ACE-QPSO-LSSVM algorithm has a better model fit (R2=0.8030), minor prediction error (mean absolute error=0.0825, mean square error=0.0110) and faster convergence (second iteration), which support that the ACE-QPSO-LSSVM algorithm emthod is more feasible and efficient in predicting slope stability.

Runoff prediction of lower Yellow River based on CEEMDAN–LSSVM–GM(1,1) model

Accurate medium and long-term runoff forecasts play a vital role in guiding the rational exploitation of water resources and improving the overall efficiency of water resources use. Machine learning is becoming a common trend in time series forecasting research. Least squares support vector machine (LSSVM) and grey model (GM(1,1)) have received much attention in predicting rainfall and runoff in the last two years. “Decomposition-forecasting” has become one of the most important methods for forecasting time series data. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition method has powerful advantages in dealing with nonlinear data. Least squares support vector machine (LSSVM) has strong nonlinear fitting ability and good robustness. Gray model (GM(1,1)) can solve the problems of little historical data and low serial integrity and reliability. Based on their respective advantages, a combined CEEMDAN–LSSVM–GM(1,1) model was developed and applied to the runoff prediction of the lower Yellow River. To verify the reliability of the model, the prediction results were compared with the single LSSVM model, the CEEMDAN–LSSVM model and the CEEMDAN–support vector machines (SVM)–GM(1,1). The results show that the combined CEEMDAN–LSSVM–GM(1,1) model has a high accuracy and the prediction results are better than other models, which provides an effective prediction method for regional medium and long-term runoff prediction and has good application prospects.

Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models

Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.

A Combined Model Based on Feature Selection and WOA for PM2.5 Concentration Forecasting

As people pay more attention to the environment and health, P M 2.5 receives more and more consideration. Establishing a high-precision P M 2.5 concentration prediction model is of great significance for air pollutants monitoring and controlling. This paper proposed a hybrid model based on feature selection and whale optimization algorithm (WOA) for the prediction of P M 2.5 concentration. The proposed model included five modules: data preprocessing module, feature selection module, optimization module, forecasting module and evaluation module. Firstly, signal processing technology CEEMDAN-VMD (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Variational Mode Decomposition) is used to decompose, reconstruct, identify and select the main features of P M 2.5 concentration series in data preprocessing module. Then, AutoCorrelation Function (ACF) is used to extract the variables which have relatively large correlation with predictor, so as to select input variables according to the order of correlation coefficients. Finally, Least Squares Support Vector Machine (LSSVM) is applied to predict the hourly P M 2.5 concentration, and the parameters of LSSVM are optimized by WOA. Two experiment studies reveal that the performance of the proposed model is better than benchmark models, such as single LSSVM model with default parameters optimization, single BP neural networks (BPNN), general regression neural network (GRNN) and some other combined models recently reported.

Using Cluster Analysis and Least Square Support Vector Machine to Predicting Power Demand for the Next-Day

Predicting the next-day power demand has been one of the most important research areas in the electricity industry for the past decade. A successful and more accurate prediction can help both the policy-makers and consumers to plan their bidding strategies. Self-organizing maps (SOM) and K-means are the two classical algorithms among many clustering ones. In this paper, they were used to predict the electric demand with the combination of the least-square support vector machine (LSSVM). The purpose of this paper is to analyze the capacity of the SOM-LSSVM by comparing it with the K-means-LSSVM and a single LSSVM for one-day-ahead electric demand forecasting. A new parallel-grid search algorithm for LSSVM is also proposed to improve orecasting speed. The empirical testing shows that all of the three models provide good prediction results, but the results of the mean absolute percentage error obtained with the SOM-LSSVM method can achieve better prediction accuracy compared with the K-means-LSSVM and a single LSSVM models.

A Multi-Stage Intelligent Model for Electricity Price Prediction Based on the Beveridge–Nelson Disintegration Approach

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.

Daily PM2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm

Increased attention has been paid to PM2.5 pollution in China. Due to its detrimental effects on environment and health, it is important to establish a PM2.5 concentration forecasting model with high precision for its monitoring and controlling. This paper presents a novel hybrid model based on principal component analysis (PCA) and least squares support vector machine (LSSVM) optimized by cuckoo search (CS). First PCA is adopted to extract original features and reduce dimension for input selection. Then LSSVM is applied to predict the daily PM2.5 concentration. The parameters in LSSVM are fine-tuned by CS to improve its generalization. An experiment study reveals that the proposed approach outperforms a single LSSVM model with default parameters and a general regression neural network (GRNN) model in PM2.5 concentration prediction. Therefore the established model presents the potential to be applied to air quality forecasting systems.

A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction

Accurate wind power generation prediction, which has positive implications for making full use of wind energy, seems still a critical issue and a huge challenge. In this paper, a novel hybrid approach has been proposed for wind power generation forecasting in the light of Cloud-Based Evolutionary Algorithm (CBEA) and Least Squares Support Vector Machine (LSSVM). In order to improve the forecasting precision, a two-way comparison approach is conducted to preprocess the original wind power generation data. The pertinent parameters of LSSVM are optimized by using CBEA to verify the learning and generalization abilities of the LSSVM model. The experimental results indicate that the forecasting performance of the proposed model is better than the single LSSVM model and all of the other models for comparison. Moreover, the paired-sample t-test is employed to cast light on the applicability of the developed model.

A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting

Annual power load forecasting is essential for the planning, operation and maintenance of an electric power system, which can also mirror the economic development of a country to some extent. Accurate annual power load forecasting can provide valuable references for electric power system operators and economic managers. With the development of Energy Internet and further reformation of electric power market, power load forecasting has become a more difficult and challenging task. In this paper, a new hybrid annual power load forecasting model based on LSSVM (least squares support vector machine) and MFO (Moth-Flame Optimization algorithm) is proposed, which the parameters of LSSVM model are optimally determined by the latest nature-inspired metaheuristic algorithm MFO. Meanwhile, the rolling mechanism is also employed. The forecasting results of China’s annual electricity consumption indicate the proposed MFO-LSSVM model shows much better forecasting performance than single LSSVM, FOA-LSSVM (LSSVM optimized by fruit fly optimization), and PSO-LSSVM (LSSVM optimized by particle swarm optimization). MFO, as a new intelligence optimization algorithm, is attractive and promising. The LSSVM model optimized by MFO can significantly improve annual power load forecasting accuracy.

A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM) ensemble model with phase space reconstruction (PSR) and particle swarm optimization (PSO), is presented. First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently. Then, all the primary prediction results are integrated to produce the most appropriate prediction results by another particular LSSVM—a multiple kernel LSSVM, which reduces the dependence of modeling accuracy on kernel function and parameters. Phase space reconstruction theory is applied to extract the chaotic characteristic of input data source and reconstruct the data sample, and particle swarm optimization algorithm is used to obtain the best LSSVM individual members. A case study is employed to verify the effectiveness of presented model with real operation data of aero engine. The results show that prediction accuracy of the proposed model improves obviously compared with other three models.

Traffic Flow Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

The accuracy of traffic flow forecasting plays an important role in the field of modern Intelligent Transportation Systems (ITS). The least squares support vector machine (LSSVM) has been shown to provide a strong potential in forecasting problems, particularly by using appropriate heuristic algorithms to determine the value of its two parameters. However, the disadvantage of these meta-heuristics is that it is difficult to understand and slowly achieve the global optimal solution. As a new heuristic algorithm, the fruit fly optimization algorithm (FOA) has the advantages of easy to understand and quickly converge to the global optimal solution. Therefore, in order to improve the prediction performance of the model, this paper presents a traffic flow prediction model based on least squares support vector machine and automatically determines the least squares support vector machine model with two parameters in the appropriate value by FOA. The experiment results show that the LSSVM combined with FOA (LSSVM-FOA) perform better than other methods, namely single LSSVM model, RBF neural network (RBFNN) and LSSVM combined with particle swarm optimization algorithm (LSSVM-PSO).

A novel denitration cost optimization system for power unit boilers

Reducing the denitration cost of coal-fired boilers is important to enhance the competitiveness of power generation companies. This study proposes a real operation data-based denitration cost optimization system that guides operators in economically adjusting the operation parameters of boilers. A data-driven least square support vector machine (LSSVM) learning method is utilized to predict the denitration cost of a coal-fired boiler. Back propagation (BP) is used here to select the input variables to simplify the model. With the pre-built BP-LSSVM-based denitration cost model, the genetic algorithm (GA) is then applied to obtain offline optimizations at the typical operating load points, which results in an Offline Optimal Expert Database (OOED). Once a load command is received, fuzzy association rule mining (FARM) is employed to extract the relationship between the operating load point and the optimal adjustable variables (AVs) in the OOED, thereby achieving the online denitration cost optimization of the power plant. For comparison, a single LSSVM method is also employed to build a denitration cost prediction model, and the GA and FARM proposed in this study are compared too. The results show that, compared with the single LSSVM method, the BP-LSSVM method not only predicts more accurately but also lowers the model complexity. In addition, considering the denitration cost, optimization time, and update time, the proposed BP-LSSVM-GA-FARM-based denitration cost optimization system is always better than traditional optimization methods.

Mid‐term electricity market clearing price forecasting using multiple least squares support vector machines

Mid-term electricity market clearing price (MCP) forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. A multiple least squares support vector machine (LSSVM) based mid-term electricity MCP forecasting model is proposed in this study. Data classification and price forecasting modules are designed to first pre-process the input data into corresponding price zones, and then forecast the electricity price. The proposed model showed improved forecasting accuracy on both peak prices and overall system compared to the forecasting model using a single LSSVM. PJM interconnection data are used to test the proposed model.

Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach

A hybrid mid-term electricity market clearing price (MCP) forecasting model combining both least squares support vector machine (LSSVM) and auto-regressive moving average with external input (ARMAX) modules is presented in this paper. Mid-term electricity MCP forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity market clearing price (MCP) forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. PJM interconnection data have been utilized to illustrate the proposed model with numerical examples. The proposed hybrid model showed improved forecasting accuracy compared to a forecasting model using a single LSSVM.

A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler

Real operation data of power plants are inclined to be concentrated in some local areas because of the operators’ habits and control system design. In this paper, a novel least squares support vector machine (LSSVM)-based ensemble learning paradigm is proposed to predict NOx emission of a coal-fired boiler using real operation data. In view of the plant data characteristics, a soft fuzzy c-means cluster algorithm is proposed to decompose the original data and guarantee the diversity of individual learners. Subsequently the base LSSVM is trained in each individual subset to solve the subtask. Finally, partial least squares (PLS) is applied as the combination strategy to eliminate the collinear and redundant information of the base learners. Considering that the fuzzy membership also has an effect on the ensemble output, the membership degree is added as one of the variables of the combiner. The single LSSVM and other ensemble models using different decomposition and combination strategies are also established to make a comparison. The result shows that the new soft FCM-LSSVM-PLS ensemble method can predict NOx emission accurately. Besides, because of the divide and conquer frame, the total time consumed in the searching the parameters and training also decreases evidently.

Knee joint vibration signal analysis with matching pursuit decomposition and dynamic weighted classifier fusion.

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis.

Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model.

A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting

Support vector machine is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in time-series forecasting. In this paper, least square support vector machine (LSSVM) is an improved algorithm based on SVM, with the combination of self-organizing maps(SOM) also known as SOM-LSSVM is proposed for time-series forecasting. The objective of this paper is to examine the flexibility of SOM-LSSVM by comparing it with a single LSSVM model. To assess the effectiveness of SOM-LSSVM model, two well-known datasets known as the Wolf yearly sunspot data and the Monthly unemployed young women data are used in this study. The experiment shows SOM-LSSVM outperforms the single LSSVM model based on the criteria of mean absolute error (MAE) and root mean square error (RMSE). It also indicates that SOM-LSSVM provides a promising alternative technique in time-series forecasting.

A study on speaker identification based on weighted LS-SVM

As one of a practical method, least squares support vector machine (LS-SVM) is usable for nonlinear separable problem as speaker identification. However, single LS-SVM can only do such classifying as binary classification, so it always needs multiple LS-SVMs and corresponding algorithms for classifying multiple speakers in a speaker identification database. By comparing pairwise LS-SVM with one-against-all LS-SVM, it is obvious that the pairwise LS-SVM has the advantage of facilitative expanding for different cases, while the one-against-all LS-SVM can not bring. However conventional pairwise LS-SVM needs too many judgment times to do multi-classing. In order to improve the pairwise LS-SVM and make it applicable to multi-speaker identification system, we propose a new notion of classification weight for pairwise LS-SVM and the corresponding algorithm, named as pairwise LS-SVM based on classification weight, i.e., the m-ωLS-SVM method, which can be used in multi-speaker identification system. Experiment results show that, comparing with conventional pairwise LS-SVM, the identification speed of the system with m-ωLS-SVM method is improved while keeping correct rate of identification, or vice versa, with only a little increase of training time.

Hybrid modeling of penicillin fermentation process based on least square support vector machine

According to the problem of the pre-estimation with least square support vector machine (LSSVM) modeling is not ideal in the initial stages of penicillin fermentation process, two hybrid models are designed by utilizing the advantage of LSSVM and kinetics model. Through selecting the appropriate state variables and adopting these methods for penicillin fermentation, the mycelial concentration can be pre-estimated. Experiment results show that these hybrid modeling methods not only improve the above problem, but also have higher predicting accuracy and more powerful generalization ability than the single LSSVM method.