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Multi-objective optimization of the Atkinson cycle gasoline engine using NSGA III coupled with support vector machine and back-propagation algorithm

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ENERGY 2023年第PartA期262卷

作者： Li, Yangyang Zhou, Shi Liu, Jingping Tong, Ji Dang, Jian Yang, Fuyuan Ouyang, Minggao Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China

This paper presents an optimization method using Non-dominated Sorting Genetic algorithm (NSGA) III to drive support vector machine (SVM). In the NSGA III algorithm, brake specific fuel consumption (BSFC), NOx and CO2 are optimized by changing the engine control parameters including spark angle, VVT-I (intake), VVT-E (exhaust) and exhaust gas recirculation (EGR). The engine GT-Power physical model is used to generate training data for the SVM, and verify the accuracy of the results of NSGA III algorithm during the optimization process. The SVM with fast calculation speed is used in the calculation of NSGA III fitness evaluation. In addition, enhancing training is utilized to improve the accuracy of the SVM model in this research. When the optimization method is applied to the Atkinson cycle gasoline engine, its high efficiency has been presented. In the three plans obtained by GT-Power physical model with all four parameters optimized, the maximum reduction rates of BSFC, NOx, CO2 and CO (g/kW.h) reached 7.07%, 35.90%, 6.62% and 5.50% respectively. The SVM model is compared with back-propagation algorithm, and the result proves that SVM is more suitable for such problems. Finally, based on the Pareto optimal solution obtained by the optimization method, it significantly promotes the solution of multiobjective optimization problems. Theoretically, the time cost of the optimization method in this paper can reach 1/23 of that for the optimization algorithm directly driving physical model.

关键词： Atkinson cycle gasoline engine NSGA III algorithm Support vector machine algorithm back-propagation algorithm Enhancing training Digital twins

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A digital background calibration scheme for non-linearity of SAR ADC using back-propagation algorithm

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MICROELECTRONICS JOURNAL 2021年 114卷 105113-105113页

作者： Lu, Hao-wei Yu, Xiao-Peng Wang, Si-Qi Liu, Yu-Yan Huang, Zhen-Yan Lu, Zheng-Hao Yeo, Kiat-Seng Chen, Jer-Ming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Soochow Univ Suzhou 215006 Peoples R China Singapore Univ Technol & Design 8 Somapah Rd Singapore 487372 Singapore

A new digital background calibration scheme for non-linearity of successive approximation register (SAR) analogto-digital convertor (ADC) is presented. Since non-linearity of high resolution SAR ADC is mainly caused by the difference between the real and ideal weights of capacitor digital-to-analog convertor (CDAC) with respect to the normalized-full-scale, calibration of weights of more significant bits is necessary when the resolution of SAR ADC comes to more than 12 bits. By using back-propagation algorithm to train the normalized real weight of more significant bits (MSBs) in neural network without any change in SAR ADC circuit design, the calibration table for each bit is implemented and updated in the digital domain without interrupting normal ADC process, which is used to correct the raw SAR code in the background to improve the performance of ADC, which is suitable for some detection applications in particular circumstances. In MATLAB simulation, the signal to noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) of a 14-bit with 1-bit redundancy SAR ADC model are improved to 85.59 dB and 97.27 dB from 56.65 dB to 77.07 dB using the proposed calibration scheme, at a standard deviation of a unit capacitor of 2%.

关键词： Successive approximation register(SAR) Analog-to-digital converter(ADC) Capacitor digital-to-analog convertor (CDAC) Linearity calibration Digital background calibration back-propagation algorithm Neural network

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A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training

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NEURAL COMPUTING & APPLICATIONS 2018年第12期30卷 3707-3720页

作者： Amirsadri, Shima Mousavirad, Seyed Jalaleddin Ebrahimpour-Komleh, Hossein Kashan Univ Dept Comp & Elect Engn 6 Km Ghotbravandi Blvd Kashan Iran

In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, eliminating the problem of getting stuck in local optimum. For this purpose, first the global search ability of the grey wolf optimizer (GWO) is improved with the Levy flight, a random walk in which the jump size follows the Levy distribution, which results in a more efficient global search in the search space thanks to the long jumps. Then, this improved algorithm is combined with back propagation (BP) to use the advantages of enhanced global search ability of GWO and local search ability of BP algorithm in training neural network. The performance of the proposed algorithm has been evaluated by comparing it against a number of well-known meta-heuristic algorithms using twelve classification and function-approximation datasets.

关键词： Grey wolf optimizer Multilayer perceptron Neural network training Learning Levy flight back-propagation algorithm

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Learning matte extraction in green screen images with MLP classifiers and the back-propagation algorithm

Learning matte extraction in green screen images with MLP cl...

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International Conference on Electronics, Communications and Computers

作者： Roberto Rosas-Romero Omar Lopez-Rincon E. David Rojas Nestor-Paul Jacobo Universidad de las Americas Puebla

ISBN: (纸本)9781509000807

This paper considers the problem of automatically extracting a foreground element with its alpha matte in green screen images by training a multi-layer perceptron (MLP) with the back-propagation algorithm. The classifier learns to identify green backgrounds, foreground object contours, and the corresponding alpha values for subsequent digital compositing. We developed our own dataset to train and test the MLP for alpha matte extraction. To speed up the generation of the training set, a second method for semi-automatic alpha matte extraction is proposed. Different experiments show that automatic matte extraction, based on the MLP, generates high-quality matting visually and it is also shown that results depend on the training and the architecture of the classifier. To the best of our knowledge, this is the first effort that applies neural networks to the problem of alpha matte extraction.

关键词： Alpha matting Digital compositing Green screen Machine learning back-propagation algorithm

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Implementation of the RBF neural chip with the back-propagation algorithm for on-line learning

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APPLIED SOFT COMPUTING 2015年 29卷 233-244页

作者： Kim, J. S. Jung, S. Samsung Elect Suwon South Korea Chungnam Natl Univ Dept Mechatron Engn ISEE Taejon South Korea

This article presents the hardware implementation of the floating-point processor (FPP) to develop the radial basis function (RBF) neural network for the general purpose of pattern recognition and nonlinear control. The floating-point processor is designed on a field programmable gate array (FPGA) chip to execute nonlinear functions required in the parallel calculation of the back-propagation algorithm. Internal weights of the RBF network are updated by the online learning back-propagation algorithm. The on-line learning process of the RBF chip is compared numerically with the results of the RBF neural network learning process written in the MATLAB program. The performance of the designed RBF neural chip is tested for the real-time pattern classification of the XOR logic. Performances are evaluated by comparing results from the MATLAB through extensive experimental studies. (C) 2014 Elsevier By. All rights reserved.

关键词： Floating-point processor RBF neural network back-propagation algorithm FPGA Nonlinear classifier

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Deterministic and probabilistic occupancy detection with a novel heuristic optimization and back-propagation (BP) based algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第2期42卷 779-791页

作者： Fatema, Nuzhat Farkoush, Saeid Gholami Hasan, Mashhood Malik, H. Univ Sultan Zainal Abidin UniSZA Fac Business & Management Terengganu Malaysia Intelligent Prognost Private Ltd Delhi India Yeungnam Univ Elect Engn Dept Gyongsan Gyeongsangbuk D South Korea Jazan Univ Coll Appl Ind Technol Elect Power Engn Technol Jazan Saudi Arabia BEARS NUS Campus Singapore Singapore

In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and back-propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA.

关键词： Gravitational search algorithm back-propagation algorithm neural network machine learning optimization occupancy smart building

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back-propagation-assisted inverse design of structured light fields for given profiles of optical force

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NANOPHOTONICS 2023年第11期12卷 2019-2027页

作者： Zhao, Xiaoshu Lin, Haoze Chen, Huajin Zheng, Hongxia Ng, Jack Fudan Univ State Key Lab Surface Phys Shanghai 200433 Peoples R China Fudan Univ Dept Phys Shanghai 200433 Peoples R China Columbia Univ Dept Comp Sci New York NY 10027 USA Guangxi Univ Sci & Technol Sch Elect Engn Liuzhou 545006 Guangxi Peoples R China Guangxi Coll & Univ Key Lab Microwave Commun & Mi Liuzhou 545006 Guangxi Peoples R China Southern Univ Sci & Technol Dept Phys Shenzhen 518055 Guangdong Peoples R China

Designing a monochromatic spatially-structured light field that recovers the pre-specified profile of optical force (OF) exerted on a particle is an inverse problem. It usually requires high dimensional optimization and involves lengthy calculations, thus remaining little studied despite decades of research on OF. We report here the first attempt to attack this inverse design problem. The modus operandi relies on the back-propagation algorithm, which is facilitated by the currently available machine learning framework, and, in particular, by an exact and efficient expression of OF that shows only polynomial and trigonometric functional dependence on the engineered parameters governing the structured light field. Two illustrative examples are presented in which the inversely designed structured light fields reproduce, respectively, a predefined spatial pattern of OF and a negative longitudinal OF in a transversely trapping area.

关键词： back-propagation algorithm inverse design optical force structured light field

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Memristive crossbar-based circuit design of back-propagation neural network with synchronous memristance adjustment

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COMPLEX & INTELLIGENT SYSTEMS 2024年第4期10卷 4883-4896页

作者： Yang, Le Ding, Zhixia Xu, Yanyang Zeng, Zhigang Wuhan Inst Technol Sch Elect & Informat Engn Guanggu 1st Rd Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Luoyu Rd Wuhan 430074 Hubei Peoples R China

The performance improvement of CMOS computer fails to meet the enormous data processing requirement of artificial intelligence progressively. The memristive neural network is one of the most promising circuit hardwares to make a breakthrough. This paper proposes a novel memristive synaptic circuit that is composed of four MOS transistors and one memristor (4T1M). The 4T1M synaptic circuit provides flexible control strategies to change memristance or respond to the input signal. Applying the 4T1M synaptic circuit as the cell of memristive crossbar array, based on the structure and algorithm of the back-propagation (BP) neural network, this paper proposes circuit design of the memristive crossbar-based BP neural network. By reusing the 4T1M memristive crossbar array, the computations in the forward-propagation process and back-propagation process of BP neural network are accomplished on the memristive crossbar-based circuit to accelerate the computing speed. The 4T1M memristive crossbar array can change all the cells' memristance at a time, accordingly, the memristive crossbar-based BP neural network can realize synchronous memristance adjustment. The proposed memristive crossbar-based BP neural network is then evaluated through experiments involving XOR logic operation, iris classification, and MNIST handwritten digit recognition. The experimental results present fewer iterations or higher classification accuracies. Further, the comprehensive comparisons with the existing memristive BP neural networks highlight the advantages of the proposed memristive crossbar-based BP neural network, which achieves the fastest memristance adjustment speed using relatively few components.

关键词： Memristive circuit Memristive neural network back-propagation algorithm Crossbar array Synchronous weight training

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Incorporation of Robust Sliding Mode Control and Adaptive Multi-Layer Neural Network-Based Observer for Unmanned Aerial Vehicles

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IEEE ACCESS 2024年 12卷 98107-98120页

作者： Akhtar, Zainab Naqvi, Syed Abbas Zilqurnain Hamayun, Mirza Tariq Ahsan, Muhammad Nadeem, Ahsan Muyeen, S. M. Oshnoei, Arman Univ Engn & Technol Mechatron & Control Engn Dept Lahore 54000 Pakistan COMSATS Univ Islamabad CUI Dept Elect & Comp Engn Lahore Campus Lahore 54000 Pakistan Aalborg Univ Dept Energy DK-9220 Aalborg Denmark Qatar Univ Dept Elect Engn Doha Qatar

The control and state estimation of Unmanned Aerial Vehicles (UAVs) provide significant challenges due to their complex and nonlinear dynamics, as well as uncertainties arising from factors such as sensor noise, wind gusts, and parameter fluctuations. Neural network-based methods tackle these problems by accurately approximating unknown nonlinearities through training on input-output data. This paper proposes an adaptive Multi-layer Neural Network (MLNN) Luenberger observer-based control for altitude and attitude tracking of a quadrotor UAV. The MLNN observer, employing a modified back-propagation algorithm, is used for the quadrotor's state estimation. The adaptive nature of the proposed observer helps mitigate the effects of parameters such as wind gusts, measurement noise, and parameter variations. Subsequently, a sliding mode controller is designed based on the observed states to track the reference trajectories. Lyapunov stability is ensured by using the modified back-propagation weight update rule for the proposed MLNN observer. Simulation results demonstrate superior tracking performance of the proposed observer compared to the Sliding Mode Observer (SMO) and a Single Hidden Layer Neural Network (SHLNN) observer, even in the presence of the aforementioned parameters.

关键词： Observers Quadrotors Noise measurement Autonomous aerial vehicles Neural networks Kalman filters Uncertainty backpropagation Sliding mode control back-propagation algorithm multiple hidden layers perceptron (MLP) neural network (NN) observer sliding mode controller (SMC) sliding mode observer UAVs

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Supervised Learning in Model Reference Adaptive Sliding Mode Control

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第6期22卷 2007-2015页

作者： Makke, Omar Lin, Feng Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

The well known back-propagation algorithm has revolutionized machine learning and artificial intelligence, particularly in neural network applications. Although gradient descent-based algorithms are utilized in control applications, they are not as prevalent as in neural network applications. This discrepancy can be attributed to the successful development of various adaptation laws which ensure system stability while meeting the required design criteria. Many of these laws can be found in model reference adaptive control (MRAC) and adaptive sliding mode control (ASMC). This paper investigates the applicability of the Brandt-Lin (B-L) learning algorithm, mathematically equivalent to the back-propagation algorithm, in adaptive control applications. We find that combining the B-L learning algorithm with SMC yields a robust controller suitable for model reference adaptive sliding mode control (MRA-SMC). The controller is applicable to linear and a class of nonlinear dynamic systems and is suitable for efficient implementation. We derive the stability criteria for this controller and conduct simulations to study the adaptation's impact on chattering. Our work exemplifies one approach to adopt the back-propagation algorithm in control applications.

关键词： Adaptive control adaptive sliding mode control (ASMC) back-propagation algorithm chattering reduction model reference adaptive control (MRAC) online learning algorithms

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