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Enhancing SHADE and L-SHADE Algorithms Using Ordered Mutation

Enhancing SHADE and L-SHADE Algorithms Using Ordered Mutatio...

引用

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Mousavirad, Seyed Jalaleddin Rahnamayan, Shahryar Sabzevar Univ New Technol Fac Engn Sabzevar Iran Ontario Tech Univ Dept Elect Comp & Software Engn Nat Inspired Computat Intelligence NICI Lab Oshawa ON Canada

ISBN: (纸本)9781728125473

Differential Evolution (DE) algorithm is an efficient population-based metaheuristic algorithm which has shown satisfactory performance in solving complex real-world optimization problems. A Success-History Based Parameter Adaptation for Differential Evolution (SHADE) is a well-established variant of DE algorithm which employs a historical performance of the successful control parameters. L-SHADE algorithm extends SHADE with a linear population size reduction strategy. The SHADE and L-SHADE algorithms employ current-to-pbest/1 strategy for evolution, whilst the order of candidate solutions is not considered in their schemes. This paper proposes an ordering strategy for SHADE and L-SHADE algorithms which has shown a satisfactory influence on the performance of both algorithms. In the first direction, we propose current-to-3order/1 strategy for SHADE algorithm, which is based on ordering three candidate solutions. In the second direction, L-SHADE algorithm is improved based on ordering two candidate solutions. called eurrent-to-pbest-2order/1. The proposed strategies can improve the performance of SHADE and L-SHADE algorithms without adding any extra significant computational cost. The proposed strategy is evaluated on CEC-2017 benchmark functions and with dimensions 30, 50, and 100. Our experimental results clearly verify the effectiveness of the proposed strategies.

关键词： Differential Evolution SHADE L-SHADE Ordering strategy

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Application of SPCA-LSSVM model in soft measurement of Gasoline dry point 13

Application of SPCA-LSSVM model in soft measurement of Gasol...

引用

13th International symposium on computational intelligence and Design (ISCID)

作者： Guo, Liying Zhang, Yu Liaoning Shihua Univ Sch Informat & Control Engn Fushun Peoples R China

ISBN: (纸本)9781728184463

A soft measurement model of least squares support vector machine based on data preprocessing for sparse principal component analysis(SPCA-LSSVM) is proposed to solve the problem that the gasoline dry point on the top of atmospheric pressure tower is difficult to measure online. This method combined sparse principal component analysis (SPCA) with the least squares support vector machine (LSSVM) method, principal component analysis (PCA) is transformed to solve the regression optimization problem with quadratic penalty by introducing the sparse constraint aiming at variable selection, in this way, the dimensions of the sparse eigenvector reduces dependency and becomes more independent, it also reduces the influence of measurement noise. The data sample processed by SPCA is used as the input of LSSVM model to establish the soft measurement model of dry point at atmospheric pressure tower top, the problem of lack of sparsity in LSSVM is solved. The simulation results showes that the SPCA-LSSVM model has higher prediction accuracy than the traditional LSSVM model, and the PCA-LSSVM model, reducing the complexity of the model, showing the superiority of the soft-sensing model.

关键词： Soft sensor PCA SPCA LSSVM model Dry point of gasoline formattin

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An Intelligent and Secure control Approach for Nonlinear Systems under Attacks

An Intelligent and Secure Control Approach for Nonlinear Sys...

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ieee symposium Series on computational intelligence (SSCI)

作者： Xiangnan Zhong Zhen Ni Florida Atlantic University Boca Raton FL USA

ISBN: (纸本)9781728190495

This paper designs an intelligent and secure control approach based on adaptive dynamic programming for a class of nonlinear systems under the actuator attacks. The designed method can monitor and identify the attacks by an established state estimator based detector. When an attack is triggered, the control process automatically switches to a game-theoretical architecture for attack mitigation. The intelligent learning process is developed for both nominal and attack conditions without the requirement of system dynamics and attack information. Neural network techniques are applied to implement the proposed method with two critic networks and the control signals are calculated accordingly. Therefore, the designed intelligent control method can reduce the computation complexity. Simulation studies and results demonstrate the necessary of attack detection and mitigation during the learning process, and also verify the effectiveness of the developed method.

关键词： System dynamics Neural networks Process control Detectors Computer architecture Stability analysis Nonlinear systems

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Robust Process Identification from Step Response Data and Parallel Implementation 3

Robust Process Identification from Step Response Data and Pa...

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3rd International Conference on Industrial Artificial intelligence, IAI 2021

作者： Han, Yucheng Liu, Qingyuan Shang, Chao Huang, Dexian Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9781665435178

Fast, robust and accurate system identification is of importance to the process industry, and identification from step response is a prevalent approach. Recently, a new method based on rank constraint using low-quality industrial data has been proposed. However, with mean square error (MSE) used as the loss function, this identification method is sensitive to outliers, which may occasionally lead to invalid models. In this paper, we propose an improved robust process identification approach from step response data based on the Huber loss, which is less sensitive to outliers than generic MSE, and leads to a higher successful rate. A tailored solution algorithm based on alternating direction method of multipliers is developed, which, however, requires heavy computational cost especially when there are massive control loops to be identified simultaneously. To address this issue, we leverage recent advances in parallel computing. We show that this solution procedure can be parallelized, which leads to significant computation savings with graphical processing units used, and thus better conforms to requirement in practical situations. Numerical studies demonstrate that our proposed method is more robust against outliers, and the parallel implementation gives a faster speed in the presence of massive data. © 2021 ieee.

关键词： Mean square error

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Model-Based and Data-Driven Contingency Filtering for Real-Time Corrective Security-Constrained Economic Dispatch 5

Model-Based and Data-Driven Contingency Filtering for Real-T...

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5th ieee Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Han, Donghai Yu, Yaowen He, Dingxin Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665434256

Security-constrained economic dispatch (SCED) is one of the most important problems in power system operations. Corrective SCED (CSCED) is a type of SCED that considers corrective capabilities of the power system and adjusts base case decisions according to post-contingency states. Because of the large scale of the CSCED problem, it is difficult for purely model-based approaches to meet the time limits in real-time practical operations. To leverage historical operation data, this paper develops a novel hybrid model-based and data-driven framework to accelerate the solution process of CSCED. In the offline stage, our previous model-based contingency filtering approach is utilized to label active statuses of contingencies against historical net load samples. In the online stage, a multi-label classifier based on the K-Nearest Neighbor (KNN) algorithm quickly generates an active contingency set corresponding to the real-time net load. This active contingency set can be used to calculate the economic dispatch decisions in one shot. In addition, the accuracy of our framework can be further improved by adding an optional contingency filtering procedure at the end. Numerical testing results on the ieee RTS-96 system demonstrate the accuracy and computational efficiency of the hybrid framework as compared to a purely model-based approach. © 2021 ieee

关键词： computational efficiency

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Adaptive Optimal control of Continuous-Time Linear Systems via Hybrid Iteration

Adaptive Optimal Control of Continuous-Time Linear Systems v...

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ieee symposium Series on computational intelligence (SSCI)

作者： Omar Qasem Weinan Gao Tao Bian Mechanical and Civil Engineering Florida Institute of Technology Melbourne FL USA WorldQuant LLC* Old Greenwich CT USA

ISBN: (纸本)9781728190495

In this paper, we propose a novel dynamic programming (DP) algorithm, under the name of hybrid iteration (HI), for continuous-time linear systems. The proposed HI approach combines the advantages of two well-known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of an initial stabilizing control policy required in PI, and at the same time it maintains a faster convergence rate compared with VI. Based on the proposed HI algorithm, a data-driven adaptive optimal controller design is also proposed. Simulation results for randomly generated continuous-time linear systems with different system orders demonstrate that the proposed HI approach can save CPU time up to 73% and reduce the number of iterations to converge up to 98% comparing with the VI approach.

关键词： Linear systems Heuristic algorithms Simulation Optimal control Dynamic programming computational intelligence Convergence

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Evolutionary Algorithm Driven Explainable Adversarial Artificial intelligence

Evolutionary Algorithm Driven Explainable Adversarial Artifi...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Veal, Charlie Lindsay, Marshall Kovaleski, Scott D. Anderson, Derek T. Price, Stanton R. Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA US Army Engineer Res & Dev Ctr Vicksburg MS 39180 USA

ISBN: (纸本)9781728125473

It is well-known that machine learning algorithms can be susceptible to undesirable effects when exposed to conditions that are not expressed adequately in the training dataset. This leads to a growing interest throughout many communities;where do algorithms and trained models break? Recently, methods such as generative adversarial neural networks and variational autoencoders were proposed to create adversarial examples that challenge algorithms. This results in artificial intelligence having higher false detections or completely losing recognition. The problem is that existing solutions, are for the most part, black boxes. Current gaps include how do we better control and understand adversarial algorithms. Herein, we propose the concept of an adversarial modifier set as an understandable and controlled way to generate adversarial examples. This is achieved by exploiting the improved evolution-constructed algorithm to identify ideal features that a victim algorithm values in imagery. These features are combined to realize a tuple library that preserves spatial relations. Last, a set of algorithmically controlled modifiers that generate the imagery are found by examining the content of the false imagery. Preliminary results are encouraging and demonstrate that this approach has benefits in both generating explainable adversarial examples, as well as shedding some insight into victim algorithm decision making.

关键词： adversarial artificial intelligence generative improved evolution-constructed algorithm machine learning

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Tackling Actuator Saturation in Fixed Point Iteration-based Adaptive control 14

Tackling Actuator Saturation in Fixed Point Iteration-based ...

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ieee 14th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Issat, Hazem Tar, Jozsef K. Obuda Univ Doctoral Sch Safety & Secur Sci Budapest Hungary Obuda Univ Antal Bejczy Ctr Intelligent Robot ABC iRob Univ Res & Innovat Ctr Budapest Hungary

ISBN: (纸本)9781728173764

The limited output of various drives means a challenge in controller design whenever the acceleration need of the "nominal trajectory to be tracked" temporarily exceeds the abilities of the saturated control system. The prevailing control design methods can tackle this problem either in a single theoretical step or in two consecutive steps. In this latter case in the first step the design happens without taking into account the actuator constraints, then apply a saturation compensator if the phenomenon of windup is observed. In the Fixed Point Iteration-based Adaptive control (FPIAC) that has been developed as an alternative of the Lyapunov function-based approach the actuator saturation causes problems in its both elementary levels: in the kinematic/kinetic level where the desired acceleration is calculated, and in the iterative process that compensates the effects of modeling errors of the dynamic system under control and that of the external disturbances. The here presented approach tackles this problem in both levels by relatively simple considerations. To illustrate the method's efficiency simulation investigations were done in the FPIAC control of a modification of the van der Pol oscillator to which an additional strongly nonlinear term was added.

关键词： Adaptive control Fixed Point Iteration Banach Space Actuator Saturation Anti-windup Van der Pol Oscillator

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An Artificial intelligence Algorithm to Automate Situation Management for Operators of Unmanned Aerial Vehicles

An Artificial Intelligence Algorithm to Automate Situation M...

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ieee International Conference on Cognitive and computational Aspects of Situation Management (ieee CogSIMA)

作者： Zak, Yuval Parmet, Yisrael Oron-Gilad, Tal Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel

ISBN: (纸本)9781728160016

Unmanned Aerial Vehicle (UAV) operators must maintain high levels of situation awareness on their area of operation. To achieve this, they use the a Command and control (C2) map, which is shared among forces, and regularly overloaded with data that is irrelevant to their operational mission. Operators require distilled information at the right timing. Yet, the existing filtering mechanisms for C2 maps are layer-based and insufficient. We propose a new approach to automatically and dynamically filter information items on the map based on environmental and mission context. To achieve this, we introduce a three-tiers artificial intelligence (AI) based algorithm (GiCoMAF), where we delineate the use of machine learning (ML) models to support UAV missions. For the GiCoMAF development, tagged data was collected in simulated experimental runs with professional UAS operators. Different types of ML models were evaluated and fitted into the algorithm. The models achieved a relatively high accuracy at modeling human preference and area of interest. The approach presented in this study can be further implemented to support time-critical spatial-temporal operational problems.

关键词： artificial intelligence human systems integration uninhabited aerial vehicles mental workload situation awareness human-automation interaction intelligent systems decision making information processing

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The Effects of Adaptive control on Learning Directed Locomotion

The Effects of Adaptive Control on Learning Directed Locomot...

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2020 ieee symposium Series on computational intelligence, SSCI 2020

作者： Diggelen, Fuda Van Babuska, Robert Eiben, A.E. Technische Universiteit Delft Netherlands Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9781728125473

This study is motivated by evolutionary robot systems where robot bodies and brains evolve simultaneously. In such systems robot 'birth' must be followed by 'infant learning' by a learning method that works for various morphologies evolution may produce. Here we address the task of directed locomotion in modular robots with controllers based on Central Pattern Generators. We present a bio-inspired adaptive feedback mechanism that uses a forward model and an inverse model that can be learned on-the-fly. We compare two versions (a simple and a sophisticated one) of this concept to a traditional (open-loop) controller using Bayesian optimization as a learning algorithm. The experimental results show that the sophisticated version outperforms the simple one and the traditional controller. It leads to a better performance and more robust controllers that better cope with noise. © 2020 ieee.

关键词： controllers

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