Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm

Khishe, Mohammad; Mohammadi, Hassan Ghasemzadeh

doi:10.1016/j.oceaneng.2019.04.013

Cited by 79 publications

(26 citation statements)

References 31 publications

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“…4) Multi-Layer Perceptron with multiple layers: Multi-Layer Perceptron (MLP) is the most common and applicable type of feedforward neural networks [15]. MLP networks consist of input layer, one or more hidden layers and output layer.…”

Section: ) Stacking Ensemblementioning

confidence: 99%

Very Short-term Wind Energy Forecasting Based on Stacking Ensemble

Moreno¹,

Silva²,

Ribeiro³

et al. 2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Wind power generation is one of the technologies of electric production which still in development in Brazil, however, it already has a great penetration in the national energy matrix, representing 13.98% of the national energy consumption in Brazil. Due to the high level of uncertainty and the chaotic fluctuations in wind speed, predictions of wind energy with high accuracy is a challenge. In this context a stacking ensemble (STACK) model is proposed to forecast the wind power generation of a turbine in a wind farm at Parazinho, RN -Brazil. The proposed model combines four different algorithms as base-learners, such as, eXtreme Gradient Boosting (xgBoost), Support Vector Machine for regression with Linear Kernel (SVR-Linear), Multi-Layer Perceptron with multiple layers (MLP) and K-Nearest Neighbors (K-NN), and one algorithm as metalearner -Support Vector Machine for regression with Radial Basis Function Kernel (SVR-RBF). To access the performance of adopted methodology, the results of STACK are compared with the results of the base-learners. Four performance measure criteria, as well as statistical tests are adopted. As results, STACK reached better results in all performance measures. Indeed, STACK and SVR-Linear are statistically equals. According to these results, applying the STACK proposed model indeed improved the forecasting when comparing with the other algorithms tested individually.

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Section: ) Stacking Ensemblementioning

confidence: 99%

Very Short-term Wind Energy Forecasting Based on Stacking Ensemble

Moreno¹,

Silva²,

Ribeiro³

et al. 2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

show abstract

“…Consequently, this has led to the development of new and advanced meta-heuristic algorithms for training MLP such as the hybrid PSO-GSA [42], PSO with Autonomous Groups (PSOAG) [43], Invasive Weed Optimiser (IWO) [44], Chemical Reaction Optimiser (CRO) [45], Stochastic Fractal Search (SFS) [46], Biogeography-Based Optimizer (BBO) [47], Adaptive Best-Mass GSA (ABMGSA) [48], Chimp Optimisation Algorithm (COA) [49], Dragonfly Optimisation Algorithm (DOA) [50], Salp Swarm Optimiser (SSO) [51], Social Spider Optimisation Algorithm (SSOA), Grey Wolf Optimisation (GWO) [41], Equilibrium Optimiser (EO) [52], Sine Cosine Algorithm (SCA) [53], Modified Sine Cosine Algorithm (MSCA) [54], Whale Optimisation Algorithm (WOA) [55], Improved WOA [56], Modified WOA [57] among others.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

et al. 2021

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Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset by first, extracting and selecting the most significant statistical time-domain features. The selected features were then grouped into distinct clusters allowing for reduced computational complexity through a comparative study of four different and frequently used categories of unsupervised clustering algorithms in fault classification. The Synthetic Minority Over Sampling Technique (SMOTE) was then employed to balance the classes of the training samples from the various clusters which then served as inputs for training the supervised GWO-MLP. To validate the proposed hybrid technique (CLUST-SMOTE-GWO-MLP), it was compared with its distinct modifications (variants). The superiority of CLUST-SMOTE-GWO-MLP is demonstrated by outperforming all the distinct modifications in terms of test accuracy and seven other statistical performance evaluation metrics (error rate, sensitivity, specificity, precision, F score, Mathews Correlation Coefficient and geometric mean). The overall analysis indicates that the proposed CLUST-SMOTE-GWO-MLP is efficient and can be used to classify multiclass and imbalanced fault conditions. Article Highlights The issue of multiclass and imbalanced class outputs is addressed for improving predictive maintenance. A multiclass fault classifier based on clustering and optimised multi-layer perceptron with grey wolf is proposed. The robustness and feasibility of the proposed technique is validated on a complex hydraulic system dataset.

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“…In addition, it is observed that SSA can balance exploration and exploitation. Owing to the distinguishing characteristics including simple code and easy implementation, it is becoming one of the most studying hot areas in algorithm fields, such as node localization in wireless sensor networks [29], the Takagi-Sugeno fuzzy logic controller design [30], the extreme learning machine optimization [31], the IIR wideband digital differentiators and integrators design [32], the photovoltaic cell models parameters identification [33], PEM fuel cells parameters extracting [34], the passive sonar target classification [35], the airfoil-based savories wind turbine optimization [36], the model predictive controller devising [37], and the soil water retention curve parameter estimation [38]. ere are different salp swarm algorithm variants that are used in many areas.…”

Section: Introductionmentioning

confidence: 99%

A Modified Salp Swarm Algorithm Based on the Perturbation Weight for Global Optimization Problems

et al. 2020

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Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments.

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Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm

Cited by 79 publications

References 31 publications

Very Short-term Wind Energy Forecasting Based on Stacking Ensemble

Very Short-term Wind Energy Forecasting Based on Stacking Ensemble

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

A Modified Salp Swarm Algorithm Based on the Perturbation Weight for Global Optimization Problems

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