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hybrid learning algorithm with pairwise scatter plotting features for utility-scale energy systems

ENERGY REPORTS 2024年 12卷 5623-5632页

作者： Wang, Siqi Alonso, David Pedraza Long, Chao Chinese Acad Sci Inst Elect Engn Beijing Peoples R China Cranfield Univ Sch Water Energy & Environm Bedford England Univ Liverpool Sch Elect Engn Elect & Comp Sci Liverpool England

Dispatch strategies of utility-scale photovoltaic (PV) battery systems have mostly relied on model-based approaches with forecasting generations and demands, due to their intermittent nature. When using data-driven method, it is a challenging process to identify the most adequate models with limited computational power. An advanced hybrid dispatch algorithm is proposed, which combines a Neural Network (NN) with optimization techniques. A sequential NN with dense hidden layers and RMSprop optimiser are integrated to fine-tune the NN parameters. Pairwise scatter plotting method is used to intuitively and efficiently identify the underlying connections of inputs. The model effectively balances accuracy and computational efficiency. Sensitivity analysis identified that electricity tariffs are the most influential feature in system operations, while time and date have minimal impacts. The method allowed the model to identify key features effectively, reducing model complexity and computational costs. Case studies found that, the hybrid algorithm outperforms the traditional methods even without PV generation and demand forecasts. The proposed method only requires a fraction of computational power compared to conventional model-based optimisation method, and around 60% of that using Long ShortTerm Memory (LSTM) method.

关键词： Computational efficiency hybrid learning algorithm Neural Network Pairwise scatter Utility-scale energy system

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hybrid learning algorithm based Modified Particle Swarm Clustering for RBF Neural Network 8

Hybrid Learning Algorithm based Modified Particle Swarm Clus...

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8th International Symposium on Computational Intelligence and Design (ISCID)

作者： Guo, Ying Wang, Hongtao Power Engn Co Henan Elect Power Survey & Design Inst Zhengzhou Peoples R China

ISBN: (纸本)9781467395878

A hybrid learning algorithm based modified particle swarm clustering for RBF neural network was proposed. Firstly to confirm the positions of hidden layer RBF centers using modified particle swarm clustering algorithm, then to train the RBF neural network and get the output layer weights by least square method. The new algorithm was used for pattern classification, function approximation and system identification and the results show that this algorithm is simpler and has high precision and efficiency.

关键词： Particle Swarm Clustering Radial Basis Function (RBF) Neural Network hybrid learning algorithm

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A robust approach for root causes identification in machining processes using hybrid learning algorithm and engineering knowledge

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JOURNAL OF INTELLIGENT MANUFACTURING 2012年第5期23卷 1833-1847页

作者： Du, Shichang Lv, Jun Xi, Lifeng Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Logist Engn Shanghai 200240 Peoples R China E China Normal Univ Sch Business Shanghai 200241 Peoples R China

To improve product quality and productivity, one of the most critical factors for most manufacturers lies in quickly identifying root causes in machining process during ramp-up and production time. Though multivariate statistical process monitoring techniques using control charts have been successfully used to detect anomalies in machining processes, they cannot provide guidelines to identify and isolate root causes. One novel robust approach for root causes identification (RCI) in machining process using hybrid learning algorithm and engineering-driven rules is developed in this study. Firstly, off-line pattern match relationships between fixture fault patterns and part variation motion patterns are derived. Then, a hybrid learning algorithm is explored for identifying the part variation motion patterns. An unknown root cause is identified and isolated using the output of hybrid learning algorithm and engineering-driven rules. Finally, the data from the real-world cylinder head of engine machining processes are collected to validate the developed approach. The results indicate that the developed approach can perform effectively for identifying root causes of fixture in machining processes. All of the analysis from this study provides guidelines in developing root causes identification systems based on hybrid learning algorithm and engineering knowledge.

关键词： Root causes identification hybrid learning algorithm Engineering-driven rules

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An efficient hybrid learning algorithm for neural network-based speech recognition systems on FPGA chip

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NEURAL COMPUTING & APPLICATIONS 2014年第7-8期24卷 1879-1885页

作者： Pan, Shing-Tai Lan, Min-Lun Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan Shu Te Univ Inst Comp Sci & Informat Engn Kaohsiung 824 Taiwan

This paper implemented an artificial neural network (ANN) on a field programmable gate array (FPGA) chip for Mandarin speech measurement and recognition of nonspecific speaker. A three-layer hybrid learning algorithm (HLA), which combines genetic algorithm (GA) and steepest descent method, was proposed to fulfill a faster global search of optimal weights in ANN. Some other popular evolutionary algorithms, such as differential evolution, particle swarm optimization and improve GA, were compared to the proposed HLA. It can be seen that the proposed HLA algorithm outperforms the other algorithms. Finally, the designed system was implemented on an FPGA chip with an SOC architecture to measure and recognize the speech signals.

关键词： Field programmable gate array Genetic algorithm hybrid learning algorithm Speech recognition

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A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks

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INFORMATION SCIENCES 2009年第13期179卷 2175-2193页

作者： Castro, Juan R. Castillo, Oscar Melin, Patricia Rodriguez-Diaz, Antonio UABC Univ Tijuana Mexico Tijuana Inst Technol Div Grad Studies & Res Dept Comp Sci Tijuana 22500 Mexico

In real life, information about the world is uncertain and imprecise. The cause of this uncertainty is due to: deficiencies on given information, the fuzzy nature of our perception of events and objects, and on the limitations of the models we use to explain the world. The development of new methods for dealing with information with uncertainty is crucial for solving real life problems. In this paper three interval type-2 fuzzy neural network (IT2FNN) architectures are proposed, with hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). At the antecedents layer, a interval type-2 fuzzy neuron (IT2FN) model is used, and in case of the consequents layer an interval type-1 fuzzy neuron model (IT1FN), in order to fuzzify the rule's antecedents and consequents of an interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). IT2-TSK-FIS is integrated in an adaptive neural network, in order to take advantage the best of both models. This provides a high order intuitive mechanism for representing imperfect information by means of use of fuzzy If-Then rules, in addition to handling uncertainty and imprecision. On the other hand, neural networks are highly adaptable, with learning and generalization capabilities. Experimental results are divided in two kinds: in the first one a non-linear identification problem for control systems is simulated, here a comparative analysis of learning architectures IT2FNN and ANF1S is done. For the second kind, a non-linear Mackey-Glass chaotic time series prediction problem with uncertainty sources is studied. Finally, IT2FNN proved to be more efficient mechanism for modeling real-world problems. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Interval type-2 fuzzy neural networks Interval type-2 fuzzy neuron hybrid learning algorithm Interval type-2 fuzzy inference systems

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Fuzzy Wavelet Neural Network With an Accelerated hybrid learning algorithm

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2012年第3期20卷 463-470页

作者： Davanipoor, Mehrnoosh Zekri, Maryam Sheikholeslam, Farid Isfahan Univ Technol Dept Elect Engn Esfahan 84154 Iran

An accelerated hybrid learning algorithm is proposed for the training of fuzzy wavelet neural networks (FWNNs). The algorithm gives the initial parameters by the clustering algorithm and then updates them with a combination of backpropagation and recursive least-squares methods. The parameters are updated in the direction of steepest descent but with a local adaptive learning rate that is different for each epoch and only depends on the sign of the gradient error function. This adaptive learning rate is chosen, such as to accelerate convergence. The learning algorithm is inspired from the halving method to find polynomials roots. Even though the results are quite satisfactory, the algorithm is much simpler than others that have been reported. In addition, it does includes no excessive terms in adapting formulation, unlike much of the research in this area. Furthermore, this algorithm somewhat increases accuracy, while using far fewer parameters. Simulation results indicate a superior convergence speed, in comparison with other training methods in FWNN.

关键词： Adaptive learning rate fuzzy wavelet neural networks (FWNNs) hybrid learning algorithm

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A new online hybrid learning algorithm of adaptive neural fuzzy inference system for fault prediction

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2015年第1期23卷 68-76页

作者： Du, Zhanlong Li, Xiaomin Mao, Qiong Mech Engn Coll Dept UAV Engn Shijiazhuang 050003 Peoples R China

Effective prognostic tools are crucial for maintenance to predict faults before systems are completely damaged and to ensure reliability. A new online hybrid learning method for adaptive neural fuzzy inference system (ANFIS) prediction model is presented in this paper based on square-root cubature Kalman filter (SCKF) and recursive least squares (RLS). SCKF and RLS are used to recursively optimise ANFIS nonlinear and linear parameters, respectively. Fault feature sensitive to system degradation process is selected as ANFIS predicting variable for fault prediction. The simulation results indicate that the higher forecasting accuracy or the lower computational complexity is obtained compared with other three online learning methods.

关键词： neural networks fuzzy systems hybrid learning algorithm nonlinear filter fault prediction square-root cubature Kalman filter SCKF recursive least squares RLS maintenance chaotic time series continuous stirred tank reactor CSTR

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Combining genetic optimisation with hybrid learning algorithm for radial basis function neural networks

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ELECTRONICS LETTERS 2003年第22期39卷 1600-1601页

作者： Guo, L Huang, DS Zhao, WB Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China

A two-step learning scheme for radial basis function neural networks (RBFNN) is proposed. A genetic algorithm initially optimises the parameters of the RBFNN and a hybrid learning algorithm adjusts these parameters fu... 详细信息

关键词： radial basis function networks genetic algorithms learning (artificialintelligence) genetic optimisation hybrid learning algorithm radial basis function neuralnetworks RBFNN RBF neural networks two-step learning scheme genetic algorithm generalisationperformance

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ANFIS based automatic voltage regulator with hybrid learning algorithm

ANFIS based automatic voltage regulator with hybrid learning...

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42nd International Universities Power Engineering Conference

作者： Mitra, P. Maulik, S. Chowdhury, S. P. Chowdhury, S. St Thomas Coll Engn & Technol Dept Elect Engn Kolkata 700023 India Meghnad Saha Inst Tech Kolkata India Univ Jadavpur Kolkata India Womens Polytech Kolkata India

ISBN: (纸本)9781905593361

In this paper, the authors propose a design methodology of Adaptive Neuro-Fuzzy Inference System (ANFIS) based Automatic Voltage Regulator (AVR) using hybrid learning algorithm to improve the small-signal performance of power system. Here, a zero order Sugeno fuzzy model is considered, whose parameters are tuned off-line through hybrid learning algorithm. This algorithm is a combination of least square estimation and error backpropagation method, where the least square method is applied for the tuning of linear output membership function parameters and the backpropagation method is used to tune the nonlinear input membership function parameters. The proposed method is verified through digital simulation with a single machine infinite bus system. It is found that the AVR is performing well in restoring the terminal voltage instantaneously and the damping characteristics of the rotor angle are also improved. The simulation results establish that the design of ANFIS based AVR employing hybrid learning algorithm can be very useful in small signal stability of power system.

关键词： AVR Adaptive Neuro-Fuzzy Inference System Sugeno-fuzzy model hybrid learning algorithm

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The study of an hybrid learning algorithm and an hybrid architecture model in agent-based simulation

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IMACS Multiconference on Computational Engineering in Systems Applications (CESA 2006)

作者： Guo Xiao-jun Yang Jian-jun Feng Guo-hu Naval Aeronaut Engn Inst Dept Control Engn Yantai Shandong Peoples R China

ISBN: (纸本)9787302139225

To improve learning efficiency, an hybrid learning algorithm is proposed to combine individual learning and group learning effectively;it improves agent's ability and system's intelligence level;to satisfy the requirements of complex adaptability, an hybrid architecture model is developed;in this model, a coordination control unit based on knowledge is proposed to coordinate the cognitive process and reactive process.

关键词： multi-agent system hybrid learning algorithm hybrid architecture

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