An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

/pdf/a-survey-on-analysis-and-design-of-model-based-fuzzy-control-3hrjlrel0k.pdf

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

Computational Analysis of Microarray Data

In this paper, a robust fuzzy credit assignment CMAC (FCA-CMAC) is proposed to speed up the learning ability and to increase the robust capability of CMAC. The FCA-CMAC is to combine the fuzzy logic concept and credit assignment ideas to provide fast and accurate learning for CMAC. With fuzzified blocks, FCA-CMAC can increase its precision and resolution; Meanwhile, the learning approach is to replace the logic AND and OR operations in the CMAC with the commonly used t-norm and t-conorm operations, respectively. Furthermore, the calculated errors are assigned proportional to the inverse of learning times, which are viewed as the creditability of addressed hypercubes. Besides, we also embed the robust learning algorithm (i.e. M-estimators) and tune the learning constant in the CMAC learning algorithm to control system of an inverse dynamic plant. From simulation results, it shows that the proposed algorithm of FCA-CMAC has better learning capability and performance than other CMACs.

A robust fuzzy credit assignment cerebellar model articulation controller (FCA-CMAC) with fast learning applied to control system

Heart rate estimation from fitness plays an important role in the evaluation of fitness exercises. Conventional approaches use the photoplethysmography (PPG) sensor to consider the change of light absorption on the wrist skin for heart rate estimation. However, users are required to buy smartwatches for using this function. Various approaches based on video analysis are recently implemented for surveillance purpose. However, it is unstable for motion scenario such as fitness exercises due to the color distortion induced by movement. POS and CHROM are introduced to address this issue. Since the fixed projection planes from POS and CHROM are given in several sources of light, it is not widely applied for surveillance applications. Therefore, a novel projection plane that is adaptively changed with the lighting environment is proposed to estimate the heart rate from fitness videos in ambient light. Moreover, image and digital signal processing techniques are also applied to extract the clean pulse signal from a novel projection plane. From the experiments conducted, the proposed approach outperformed the existing approaches to be the best model for heart rate estimation from fitness videos with the accuracy up to 91.08%.

Color Distortion Removal for Heart Rate Monitoring in Fitness Scenario

The synchronization control problem for a class of nonlinear complex dynamical networks with network attacks and actuator faults is addressed in this paper. The main contributions are as follows: (1) The developed controller is capable of compensating for nonlinearity and actuator faults simultaneously; (2) Compared with existing network attacks results, the input matrix considered in this paper is more general. To be more specific, by approximating the nonlinear uncertainty by a fuzzy logical system (FLS), a new FLS-based distributed adaptive controller is developed. By virtue of estimating the norm of weight vector in the FLS, the developed controller can compensate for the effect of nonlinearity under actuator faults. By using matrix factorization technique and sliding mode control method, the developed method is shown to be resilient to network attacks. Moreover, it is proven that the developed controller can guarantee asymptotic convergent of synchronisation errors. Finally, an illustrative example is used to demonstrate the effectiveness and efficiency of the proposed method.

FLS-Based Fuzzy Synchronization Control of Complex Dynamical Networks Under Network Attacks and Actuator Faults

This paper is concerned with establishing an adaptive ﬁxed-time control strategy for nonstrict-feedback nonlinear systems with event-triggered mechanism, which makes sure that the upper bound of the convergence time can be known in advance and is independent of the initial states. Firstly, the piecewise functions and the command ﬁltered technique are separately utilized to avoid the emergence of singularity problem and computational complexity problem. Secondly, neural networks are not only exploited to approximate unknown nonlinear functions and further deal with nonstrict-feedback structures but also assisted in achieving ﬁxed-time stability with the application of command ﬁlter. Then, the construction of event-triggered control signal by applying relative threshold strategy is eﬀectual means of saving communication resources, and the design of an estimator that identiﬁes the adaptive parameter can eﬀectively dodge the occurrence of over-parametrization. Furthermore, the proposed control algorithm enables that: (1) the tracking error converges near the origin in a ﬁxed time; (2) all signals of the closed-loop system remain bounded; (3) Zeno behavior will not occur. Finally, the validity of the proposed control strategy is veriﬁed via a practical example of the electromechanical system and a comparative simulation.

Neuroadaptive Fixed-time Command Filtered Control for Nonstrict-feedback Nonlinear Systems under Event-triggered Mechanism

The previous step CMAC for online tuning robust fuzzy controllers is proposed in this paper. There are two processes in the proposed schemes: one is the robust fuzzy controller and the other is the previous step CMAC learning algorithm. The robust fuzzy controller can achieve a certain goal without concern for instability of the controlled system in the presence of significant plant uncertainties, if the nominal parameter is roughly estimated. In order to improve the performance of robust fuzzy controller, the nominal parameter should be adjusted. Thus, a previous step CMAC learning algorithm under the robust fuzzy control structure is employed for online tuning the nominal parameter. Finally, simulation results demonstrate the excellent capability of the proposed schemes for improving the output performance.

http://ieeexplore.ieee.org/iel5/6246741/6257133/06257144.pdf

Shun-Feng Su

Papers

A robust fuzzy credit assignment cerebellar model articulation controller (FCA-CMAC) with fast learning applied to control system

Color Distortion Removal for Heart Rate Monitoring in Fitness Scenario

FLS-Based Fuzzy Synchronization Control of Complex Dynamical Networks Under Network Attacks and Actuator Faults

Neuroadaptive Fixed-time Command Filtered Control for Nonstrict-feedback Nonlinear Systems under Event-triggered Mechanism

The previous step CMAC for online tuning robust fuzzy controllers