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Proceedings of the 2013 ieee symposium on computational intelligence in control and automation, CICA 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

Proceedings of the 2013 IEEE Symposium on Computational Inte...

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2013 3rd ieee symposium on computational intelligence in control and automation, CICA 2013 - 2013 ieee symposium Series on computational intelligence, SSCI 2013

ISBN: (纸本)9781467358934

The proceedings contain 31 papers. The topics discussed include: enhanced monitoring using PCA-based GLR fault detection and multiscale filtering;a robust fault detection scheme with an application to mobile robots by using adaptive thresholds generated with locally linear models;fault diagnosis of induction motor using CWT and rough-set theory;a new eye gaze detection algorithm using PCA features and recurrent neural networks;path planning of a data mule in wireless sensor network using an improved implementation of clustering-based genetic algorithm;study on a combined scheme by using T-S fuzzy and TSMC approaches;robust output feedback control of T-S fuzzy time-delay systems;and optimization of fuzzy logic controllers with rule base size reduction using genetic algorithms.

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ieee SSCI 2011 - symposium Series on computational intelligence - CICA 2011 - 2011 ieee symposium on computational intelligence in control and automation

IEEE SSCI 2011 - Symposium Series on Computational Intellige...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence in control and automation, CICA 2011

ISBN: (纸本)9781424499038

The proceedings contain 25 papers. The topics discussed include: intelligent PD-type fuzzy controller design for mobile satellite antenna tracking system with parameter variations effect;L-infinity analysis of Hopfield networks;optimization of model predictive control by means of sequential parameter optimization;fuzzy adaptive control for a class of nonlinear systems with unknown control gain;on control-specific derivation of affine Takagi-Sugeno models from physical models: assessment criteria and modeling procedure;nonlinear modeling of an aerobic wastewater treatment process using Laguerre-based fuzzy system;decentralized direct I-term fuzzy-neural control of an anaerobic digestion bioprocess plant;a novel training method based on variable structure systems theory for fuzzy neural networks;generalized H2 filter design for T-S fuzzy systems with quantization and packet loss;and power system stabilizer tuning study of east-central power system in Saudi Arabia.

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ieee SSCI 2014 - 2014 ieee symposium Series on computational intelligence - CICA 2014: 2014 ieee symposium on computational intelligence in control and automation, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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4th ieee symposium on computational intelligence in control and automation, CICA 2014

ISBN: (纸本)9781479945313

The proceedings contain 32 papers. The topics discussed include: extreme learning ANFIS for control applications;collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers;real-time nonlinear modeling of a twin rotor MIMO system using evolving neuro-fuzzy network;adaptive dynamic output feedback control of Takagi-Sugeno fuzzy systems with immeasurable premise variables and disturbance;optimal robust control for generalized fuzzy dynamical systems: a novel use on fuzzy uncertainties;quadrotor control using dynamic feedback linearization based on piecewise bilinear models;ultra high frequency polynomial and sine artificial higher order neural networks for control signal generator;dissolved oxygen control of activated sludge biorectors using neural-adaptive control;and cascaded free search differential evolution applied to nonlinear system identification based on correlation functions and neural networks.

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2009 ieee symposium on computational intelligence in control and automation, CICA 2009 - Proceedings

2009 IEEE Symposium on Computational Intelligence in Control...

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2009 ieee symposium on computational intelligence in control and automation, CICA 2009

ISBN: (纸本)9781424427529

The proceedings contain 20 papers. The topics discussed include: tutorial CICA-T: computing with intelligence for identification and control of nonlinear systems;orthogonal-type robot with a CAD/CAM-based position/force controller;modeling and control of a nonholonomic wheeled mobile robot with wheel slip dynamics;adaptive robot manipulator control based on plant-controller model reference using soft computing and performance index analyzer;influencing customers through customers - simulation of herd behavior in supermarkets;redundancy approach for fuzzy Lyapunov stabilization of Takagi-Sugeno descriptors;effect of weighting parameters on dynamical behavior of Hopfield neural networks with logistic map activation functions;learning functions generated by randomly initialized MLPs and SRNs;and fuzzy differential inclusion in neural modeling.

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ieee CICA 2011 committee symposium on computational intelligence in control and automation (ieee CICA 2011)

IEEE CICA 2011 committee symposium on computational intellig...

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ieee symposium on computational intelligence in control and automation, CICA

The purpose of 2011 ieee symposium on computational intelligence in control and automation is to bring together researchers, engineers, practitioners, and students from around the world to discuss the latest advances in the theory and application of computational intelligence in control and automation. It is very glad to see the symposium has received many new research results from various topics and applications of computational intelligence in control and automation, including new theories and methods of model based fuzzy systems and control, CI based predictive and adaptive control, neural networks and evolutionary computation for system learning and control, decision analysis and support systems, with applications ranging from robots, energy systems, industrial automations, wastewater treatment process, secure communication system, to human behaviors modeling.

关键词： Fuzzy systems automation Intelligent control USA Councils Analytical models computational modeling

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Proceedings of the 1998 ieee International symposium on Intelligent control (ISIC) held jointly with ieee International symposium on computational intelligence in Robotics and automation (CIRA) Intelligent Systems and Semiotics (ISAS) (Cat. No.98CH36262))

Proceedings of the 1998 IEEE International Symposium on Inte...

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ieee International symposium on Intelligent control (ISIC)

Presents the title page of the proceedings record.

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LLM-Driven Cognitive Modeling for Personalized Travel Generation

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ieee TRANSACTIONS ON computational SOCIAL SYSTEMS 2025年

作者： Ge, Shichao Ye, Peijun Zhang, Renrui Zhou, Min Dong, Hairong Wang, Fei-Yue Beijing Jiaotong Univ Sch Automation & Intelligence Beijing 100044 Peoples R China Chinese Acad Sci Inst Automation Beijing 100190 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automation Qingdao 266590 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes, and other factors. Large language model (LLM), by contrast, has significantly enhanced the anthropomorphic and personalized features of intelligent systems. To integrate their advantages, this article proposes LLM-driven cognitive modeling to generate more diverse and personalized travel demands. The new method sufficiently exploits LLM such as the llama as a basis and provides personalized travel plans so that more heterogenous travel demands could be generated. Additionally, introducing LLM into cognitive modeling can significantly reduce the time of model development, thus accelerating the research or engineering deployment. By calibrating and testing with one month's data from public transportation (buses and subways) in Beijing, our method, compared to traditional cognitive models, not only achieves better accuracy in reproducing typical travel patterns, but also generates more diverse ones, providing a more comprehensive input for computational experiments on traffic management and control strategies.

关键词： computational modeling Decision making Accuracy Planning automation Computer architecture Traffic control Memory modules Cognition Adaptation models Agent-based model (ABM) cognitive model large language model (LLM) travel behavior

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Knowledge As Not Only Justified True Beliefs in Vision

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ieee/CAA Journal of Automatica Sinica 2025年第2期12卷 297-299页

作者： Wenbo Zheng Fei-Yue Wang IEEE School of Computer Science and Artificial Intelligence Wuhan University of Technology Sanya Science and Education Innovation Park of Wuhan University of Technology the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

computational knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief. To further the previous research, we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.

关键词： computational modeling Visualization Artificial intelligence Data models Visual perception Visual databases Training data

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Effective Probabilistic Neural Networks Model for Model-Based Reinforcement Learning USV

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ieee TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2025年 22卷 11625-11641页

作者： Huang, Wenjun Cui, Yunduan Li, Huiyun Wu, Xinyu Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sys Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Hong Kong Macao Joint Lab Human Machine Shenzhen 518055 Guangdong Peoples R China

Gaussian process (GP) offers a robust solution for modeling the dynamics of unmanned surface vehicles (USV) in model-based reinforcement learning (MBRL). However, the rapidly increasing computational complexity with a large sample capacity of GP limits its application in complex scenarios that require substantial samples to cover the state space. In this article, a novel probabilistic MBRL approach, probabilistic neural networks model predictive control (PNMPC) is proposed to tackle this issue. With an iterative learning framework, PNMPC properly models the USV dynamics using neural networks from a probabilistic perspective to avoid the computational complexity associated with sample capacity. Employing this model to effectively propagate system uncertainties, a model predictive control (MPC) policy is developed to robustly control the USV against external disturbances. Evaluated by position-keeping and multiple targets-tracking scenarios on a real USV data-driven simulation, the proposed method consistently demonstrates its significant superiority in both model accuracy and control performance compared to not only GP model-based approaches but also the probabilistic neural networks-based MBRL baselines, across various scales of external disturbances.

关键词： computational modeling Probabilistic logic Neural networks Uncertainty Predictive models Vehicle dynamics Training Stability analysis computational complexity Adaptation models Reinforcement learning (RL) neural networks predictive models marine vehicles

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Lightweight Approach for Nonlinear State-Space System Identification Subjected to Skewed Measurement Noise

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ieee TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2025年 22卷 11968-11981页

作者： Liu, Xin Hai, Yang Dai, Wei China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

In this paper, the skewed output noise is considered and we propose a lightweight robust algorithm for nonlinear state-space system identification based on the generalized hyperbolic variance gamma (GHVG) distribution, which enhances the robustness of the proposed algorithm. To facilitate the realization of the proposed algorithm, the hidden variables are introduced to decompose the GHVG distribution into the Gaussian gamma mixture (GGM) distribution, which improves the computational efficiency of the proposed algorithm. The expectation maximization (EM) and the particle smoothing (PS) approaches are combined to solve the hidden variables and unknown states problems, which contributes to derive the estimation formulas of the model parameters and noise parameters simultaneously. To further reduce the computational burden of PS method for estimating the nonlinear states, a novel nearest neighbor idea is used in the identification process which ensures the performance of the proposed algorithm while reducing the number of particles involved in the calculation of the cost function. Finally, the verification results are fairly carried out to demonstrate the effectiveness of the proposed algorithm.

关键词： Noise Noise measurement Heavily-tailed distribution computational modeling Data models State-space methods automation Gamma distribution Delay effects Inference algorithms Nonlinear state-space model skewed output noise generalized hyperbolic variance gamma distribution lightweight particle smoothing

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