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A general framework for constructing the feasibility-constraint function of lamination parameters in optimization

STRUCTURES 2023年 58卷

作者： Fan, Kuan Huang, Zhengdong Wu, Hao Liu, Qinghua Hu, Yuechen Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China

Lamination parameters are extensively employed as design variables in the optimization design of composite laminates. The feasibility-constraint function of the lamination parameters in optimization is essential since it is related to the accuracy of the optimal results. In the existing literature, the feasible region for lamination parameters is expressed by multiple inequalities, which is inaccurate and unsmooth at some corners and tedious in the cases of high dimensions. This paper aims at proposing a general framework for constructing the feasibilityconstraint functions in optimization for any given set of lamination parameters with higher precision compared to the functions given by the inequalities. The feasibility-constraint function is generated by training an artificial neural network with the backpropagation algorithm based on the idea of the level set method. The training data mainly comes from the boundary points shaped by the hyperplanes calculated by a variational method. The proposed framework consists of four parts: obtaining the hyperplanes, generating the boundary points of the feasible region, constructing the feasibility-constraint function, and evaluating the accuracy of the generated function. Several experiments are implemented to validate the proposed method in the cases of four-, six-, and eight-dimensional lamination parameters. The 4D results show that the generated feasibility-constraint functions have the advantage in precision compared to the existing constraint functions in literature in the real application of structural optimization. Besides, in the 6D and 8D cases, the smoothness and accuracy of the generated feasible boundaries are also shown. The performance of enforcing the generated constraint in optimization is also studied in all three cases, which indicates that the generated constraint functions are applicable in the optimization design of composite laminates.

关键词： Lamination parameters Layup optimization Feasibility-constraint function back-propagation algorithm

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Self-organized Criticality via Retro-Synaptic Signals

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FRONTIERS IN PHYSICS 2017年第JAN期4卷

作者： Hernandez-Urbina, Victor Herrmann, J. Michael Univ Edinburgh Inst Percept Act & Behav Sch Informat Edinburgh Midlothian Scotland

The brain is a complex system par excellence. In the last decade the observation of neuronal avalanches in neocortical circuits suggested the presence of self-organized criticality in brain networks. The occurrence of this type of dynamics implies several benefits to neural computation. However, the mechanisms that give rise to critical behavior in these systems, and how they interact with other neuronal processes such as synaptic plasticity are not fully understood. In this paper, we present a long-term plasticity rule based on retro-synaptic signals that allows the system to reach a critical state in which clusters of activity are distributed as a power-law, among other observables. Our synaptic plasticity rule coexists with other synaptic mechanisms such as spike-timing-dependent plasticity, which implies that the resulting synaptic modulation captures not only the temporal correlations between spiking times of pre- and post-synaptic units, which has been suggested as a requirement for learning and memory in neural systems, but also drives the system to a state of optimal neural information processing.

关键词： neuronal avalanches self-organized criticality complex networks phase transitions synaptic plasticity retro-synaptic signals spike-timing-dependent plasticity back-propagation algorithm

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Recurrent context layered radial basis function neural network for the identification of nonlinear dynamical systems

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NEUROCOMPUTING 2024年 580卷

作者： Kumar, Rajesh Natl Inst Technol Kurukshetra Dept Elect Engn Kurukshetra 136119 India

This paper proposes a novel recurrent context layered radial basis function neural network (RCLRBFNN) for the identification of nonlinear dynamical systems. The proposed model consists of an additional context layer in which the nodes represent the unit-delayed outputs of the hidden layer radial centers. These delayed outputs undergo a nonlinear transformation by applying a tangent hyperbolic function. These transformed signals connect to the output layer neuron through the adjustable context layered weights. To tune the parameters of the proposed model the update equations are derived using the dynamic back -propagation algorithm. Further, an adaptive learning rate scheme is proposed to improve the performance of the learning algorithm. In the simulation experiment, a total of two examples are considered to test the efficacy and performance of the proposed model. The performance comparison is made with the conventional structure of the radial basis function neural network (RBFNN), Jordan Recurrent neural network (JRNN), and the feed-forward neural network (FFNN) (which is nothing but a single-layered multi-layered perceptron). Both the disturbance signal as well as system's uncertainty scenarios are considered to test the robustness shown by the proposed model. The results showed that the proposed model has delivered a better identification accuracy as compared to the other neural models.

关键词： Recurrent radial basis function neural network Nonlinear system identification back-propagation algorithm Adaptive learning rate Feed-forward and Jordan recurrent neural network

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PREDICTION OF THE ANTARCTIC MARGINAL ICE ZONE EXTENT BASED UPON ITS MULTIFRACTAL PROPERTY

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2021年第2期29卷

作者： Liu, Yue Pang, Xiaoping Zhao, Xi Stein, Alfred Zhang, Xiang Ji, Qing Qu, Meng Dong, Jiang Wuhan Univ Chinese Antarctic Ctr Surveying & Mapping Wuhan 430079 Peoples R China Wuhan Univ Key Lab Polar Surveying & Mapping Natl Adm Surveying Mapping & Geoinformat Wuhan 430079 Peoples R China Univ Twente Fac Geoinformat Sci & Earth Observat ITC Enschede Netherlands Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Tianjin Hydrog Ctr Nav Guarantee Ctr North China Sea Tianjin 300222 Peoples R China

The marginal ice zone (MIZ) around Antarctica is an important air-ice-ocean-wave interaction area and a crucial habitat for marine life. Given its dynamic nature, it is essential to understand and quantify both the short- and long-term changes of its extent. In this study, we investigated the MIZ extent time series using multifractal spectrum and R/S analysis, examining Antarctica as a whole and per sub-region. The MIZ extent was predicted using a back-propagation (BP) algorithm. The results show that the wide multifractal spectrum width of the MIZ extent time series has strong multifractal features for the entire Antarctic and for the five sub-regions. By comparing the spectrum width of the original time series with those of a shuffled series, we found that multifractality is related to long-range correlations in the time series. The Hurst exponent obtained using the R/S analysis indicates that the long-range correlation over six months is strongest for all time intervals. Compared to the autoregressive integrated moving average model, the mean absolute percentage error obtained using the BP algorithm was lower by 4-15%. We conclude that the BP algorithm combined with the multifractal property is well suited to predict the Antarctic MIZ extent.

关键词： Marginal Ice Zone Fractal Analysis Long-Range Correlation Prediction back-propagation algorithm

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Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network

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INTERNATIONAL JOURNAL OF FATIGUE 2020年第0期135卷 105527-000页

作者： Barbosa, Joelton Fonseca Correia, Jose A. F. O. Freire Junior, R. C. S. De Jesus, Abilio M. P. Univ Fed Rio Grande do Norte Natal RN Brazil Univ Porto Fac Engn CONSTRUCT Porto Portugal Fed Rural Univ Semiarid Mossoro Brazil Univ Porto Fac Engn INEGI Porto Portugal

The mean stress effect plays an important role in the fatigue life predictions, its influence significantly changes high-cycle fatigue behaviour, directly decreasing the fatigue limit with the increase of the mean stress. Fatigue design of structural details and mechanical components must account for mean stress effects in order to guarantee the performance and safety criteria during their foreseen operational life. The purpose of this research work is to develop a new methodology to generate a constant life diagram (CLD) for metallic materials, based on assumptions of Haigh diagram and artificial neural networks, using the probabilistic Stussi fatigue S-N fields. This proposed methodology can estimate the safety region for high-cycle fatigue regimes as a function of the mean stress and stress amplitude in regions where tensile loading is predominance, using fatigue S-N curves only for two stress R-ratios. In this approach, the experimental fatigue data of the P355NL1 pressure vessel steel available for three stress R-ratios ( - 1, - 0.5, 0), are used. A multilayer perceptron network has been trained with the back-propagation algorithm;its architecture consists of two input neurons (sigma(m), N) and one output neuron (sigma(a)). The suggested CLD based on trained artificial neural network algorithm and probabilistic Stussi fatigue fields applied to dog-bone shaped specimens made of P355NL1 steel showed a good agreement with the high-cycle fatigue experimental data, only using the stress R-ratios equal to 0 and -0.5. Furthermore, a procedure for estimating the fatigue resistance reduction factor, K-f, for the fatigue life prediction of structural details (stress R-ratios equal to 0, 0.15 and 0.3) in extrapolation regions is suggested and used to generate the K-f results for stress R-ratios from -1 to 0.3, based on machine learning artificial neural network algorithm.

关键词： Fatigue Artificial neural network back-propagation algorithm Stussi model Constant life diagram

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Artificial Neural Network Prediction of Overtopping Rate for Impermeable Vertical Seawalls on Coral Reefs

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JOURNAL OF WATERWAY PORT COASTAL AND OCEAN ENGINEERING 2020年第4期146卷

作者： Liu, Ye Li, Shaowu Zhao, Xin Hu, Chuanyue Fan, Zhufeng Chen, Songgui Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China MOT Tianjin Res Inst Water Transportat Engn Tianjin 300456 Peoples R China CCCC First Harbor Consultants Co Ltd Tianjin 300222 Peoples R China

An artificial neural network (ANN) tool trained using a backpropagation algorithm was developed to predict the overtopping rate of impermeable vertical seawalls on coral reefs. The training database was produced from simulations of a nonhydrostatic wave model calibrated using a subset of experimental overtopping data and covered a wide range of hydrological conditions, reef morphologies, and seawall heights. The ANN configuration was optimized through sensitivity analysis and overfitting was prevented using thek-fold cross-validation technique. The generalization ability of the ANN tool was tested against the remaining subset of the experimental data. The ANN tool provided reliable predictions using deep water wave parameters as input rather than parameters for waves at the toes of structures. This made it a practical predictor for use in the preliminary design of vertical seawalls and real time forecasting of wave-induced flooding in coral reef environments.

关键词： Vertical seawall Overtopping Coral reef Artificial neural network back-propagation algorithm SWASH

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Prediction of Solitary Wave Forces on Coastal Bridge Decks Using Artificial Neural Networks

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JOURNAL OF BRIDGE ENGINEERING 2018年第5期23卷 4018023.1-4018023.1页

作者： Xu, Guoji Chen, Qin Chen, Jianhua Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Louisiana State Univ Div Comp Sci & Engn Baton Rouge LA 70803 USA Univ Notre Dame NatHazModeling Lab South Bend IN 46556 USA Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA Northeastern Univ Dept Marine & Environm Sci Boston MA 02115 USA LA State Univ Div Comp Sci & Engn Baton Rouge LA 70803 USA

This study proposes an alternative and competitive methodology for predicting solitary wave forces on coastal bridge decks using artificial neural networks (ANNs). It is imperative to accurately predict the on-deck wave forces for the design and retrofit of coastal bridges subject to the potential impact of hurricanes and tsunamis. For this purpose, ANNs are used to determine wave loads based on a valid data set. First, the structural, fluid, and wave variables involved in the bridge deck-wave interaction are briefly introduced. The back-propagation network (BPN) wave force prediction model trained using the back-propagation algorithm is highlighted. A data set with 472 evidence cases is prepared based on extensive computational fluid dynamics (CFD) simulations. Three major input variables, the still-water level (SWL), bottom elevation of the girder/superstructure, and wave height, are selected. Then, the procedures of training the ANNs for the vertical and horizontal forces are presented in detail. Finally, the trained network structures with high predictive skills after substantial training are given with a proposed predictive equation for the vertical and horizontal forces. The results showed that the ANN methodology is robust and capable of capturing the underlying physical complexity in the bridge deck-wave interaction.

关键词： Solitary wave forces Coastal bridge decks Artificial neural networks (ANNs) back-propagation algorithm Multilayer perceptron

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Effective and efficient mapping of color appearance

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COLOR RESEARCH AND APPLICATION 1997年第5期22卷 308-317页

作者： Boldrin, E Campadelli, P Schettini, R CNR INST MULTIMEDIA INFORMAT TECHNOLNATL RES COUNCILI-20131 MILANITALY UNIV MILAN DIPARTIMENTO SCI INFORMAZMILANITALY

The reproduction of color across different media and viewing condition requires color-appearance modeling. In order to obtain an effective and efficient mapping of color appearance, the authors define here a method to approximate the combination of the forward and reverse Hunt94 color-appearance models. The method consists in learning by examples the Hunt94 color mapping for each desired experimental setup. Learning is done by feed-forward neural networks trained with the back-propagation algorithm on training sets derived from the ANSI IT8 7.2 color target. Experimental results confirm the feasibility of the method. (C) 1997 John Wiley & Sons, Inc. Inc.

关键词： cross-media color reproduction color appearance Hunt94 model feed-forward neural networks back-propagation algorithm

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An Efficient JAYA algorithm with Levy Flight for Non-linear Channel Equalization

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 145卷 112970-112970页

作者： Ingle, Kishor Kisan Jatoth, Ravi Kumar Natl Inst Technol Dept Elect & Commun Engn Warangal 506004 Andhra Pradesh India

Neural network (NN) based equalizers are known to outperform the linear equalizers based on finite impulse response (FIR) adaptive filter for highly dispersive and non-linear channels. To overcome the limitations of the back-propagation (BP) algorithm, metaheuristic algorithms are emerging as promising alternatives for training the NN based equalizers. JAYA is a simple and efficient metaheuristic algorithm. Hence, its application to channel equalization problem is worth investigating. Despite its simplicity and efficiency, the JAYA algorithm has problems such as being trapped in local minima due to insufficient diversity of population and weak exploration capability. To alleviate these issues, in this paper the concept of Levy flight (LF) and greedy selection scheme has been incorporated into the basic JAYA algorithm. The LF concept enhances the population diversity and thus avoids the state of stagnation. The greedy selection scheme is employed to improve the exploitation ability without loss of population diversity. Furthermore, in order to maintain the balance between the exploration and exploitation capabilities of the algorithm, an adaptive Levy index is proposed based on a linear control parameter strategy. An extensive simulation-based sensitivity analysis of proposed method called JAYA algorithm with Levy flight (JAYALF) with respect to key parameters is carried out to select the optimized values for these parameters. In order to validate the local optima avoidance ability, exploitation and convergence rate of the proposed JAYALF algorithm, it is tested on seventeen well-known unimodal and multimodal benchmark functions and to verify the effectiveness of the JAYALF for non-linear channel equalization problem, three wireless communication channels with two different nonlinearities have been considered for simulation. In addition, the non-parametric pairwise Wilcoxon rank-sum test has been employed to test the statistical validity of the results obtained from

关键词： Non-linear channel equalization Functional link artificial neural network back-propagation algorithm JAYA algorithm

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Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer

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Materials Today: Proceedings 2021年 45卷 7615-7621页

作者： Weam El Merrassi Abdelouahed Abounada Mohamed Ramzi Laboratory of Automatic Energy Conversion and Microelectronics (LACEM) Department of Electrical Engineering Faculty of Science and Technology University of Sultan Moulay Slimane Beni Mellal BP 23000. Morocco

High-performance field-oriented motor control requires accurate knowledge of flux and speed information. Furthermore, the elimination of sensors leads to reduced overall cost and size of the electric drive system and subsequently improving its reliability. So far, Speed sensorless control of induction motors has been faced with various techniques of speed estimation. However, the main drawbacks of these models are their insufficient performance at low speeds, along with sensitivity to parameters variation, which engenders an unsteady drive system. This paper proposes an effective sensorless indirect Field Oriented control scheme for an induction motor drive. The proposed speed observer combines artificial intelligence and model reference adaptive system (MRAS). This observer is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation. This conjunction is intended to enhance the conventional MRAS performance especially in low-speed regions and reduce its sensitivity to noise and system uncertainties as well. Otherwise, the effectiveness of the proposed speed estimator is tested in simulation using Matlab/Simulink software environment. The control structure is checked in the low speed with load variation.

关键词： Artificial neural network (ANN) Model reference adaptive system (MRAS) Induction machine Speed sensorless control back-propagation algorithm

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