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, immunity based genetic algorithm is proposed to solve quadratic assignment problem (QAP). The QAP problem, known as NP-hard problem, is a combinatorial problem found in the optimal assignment of facilities to allocations. The proposed algorithm is to enhance the performance of genetic algorithms by embedded immune systems so as to have locally optimal offspring, and it is successfully applied to solve QAP. From our simulations for those tested problems, the proposed algorithm has the best performances when compared to other existing search algorithms.

Immunity based genetic algorithm for solving quadratic assignment problem (QAP)

A slow time-delay assumption restricts the application of control approaches for numerous systems which are constantly affected by multiple uncertainties, including parameters, control coefficients, and the asymmetric dead-zone input. This work presents a new adaptive method for a class of high-order nonlinear delayed systems by removing the so-called slow time-delay assumption and multiple uncertainties. Remarkably, with a novel Lyapunov-Razumikhin (L-R) function and a direct fuzzy adaptive regulation scheme, a memoryless adaptive feedback controller is skillfully constructed to guarantee that the output tracks the given reference signal while keeping the boundedness of all closed-system signals. Finally, the presented scheme is applied to control a single-link robot system.

Direct Fuzzy Adaptive Regulation for High-Order Delayed Systems: A Lyapunov-Razumikhin Function Method.

Thispaperproposes an observer-based adaptive fuzzy-neural controller, structured bya mergedfuzzy-neural network(merged-FNN) toreducethenumberofadjustable parameters. Inthis paper, themerged-FNN isproved totakethe placeofthetraditional fuzzy-neural networks undersome assumptions. Moreover, theoverall adaptive schemes using the proposed merged-FNN guarantees thatallsignals involved are boundedand the outputof the closed-loop system asymptotically tracks thedesired outputtrajectory. From experimental examples, theproposed merged-FNN hasfar fewerparameters thanthetraditional FNN, and the computation timeissignificantly reduced. Todemonstrate the effectiveness oftheproposed methods, simulation results are illustrated inthis paper.

A MergedFuzzy-Neural NetworkandItsApplication in Fuzzy-Neural Control

Recently, the interval-valued data analysis is popular research topic in symbolic data analysis (SDA). For some of applications, it is natural to use interval-valued data because of uncertainty existing in the measurements, variability for defining a term (the minimum and the maximum temperature during a day), extremely behavior description (maximum wind speed in a given country), etc. However, the obtained data are always subject to outliers in some of applications. Moreover, the outliers may occur due to various reasons, such as erroneous measurements or noisy data from the tail of noise distribution functions. In order to handle the interval-valued data with outliers, a novel approach, called the robust interval support vector interval regression networks (RISVIRNs), is proposed. The RISVIRNs is extended from our previous work (e.g. the support vector interval regression networks; SVIRNs). It is easy to find that SVIRNs can have interval-valued data for outputs, but only can deal with the crisp input. Moreover, the outlier's effects are not discussed in SVIRNs. Hence, the support vector regression with interval-valued for input data (SVRI2) approach is proposed to determine the initial structure of RISVIRNs and to remove outliers form the interval-valued data set. Due to such approach can provide a better initial structure of RISVIRNs, the proposed approach can have fast convergent speed and robust against outliers. The experimental results with real data sets show the validity of the proposed RISVIRNs.

Robust interval support vector interval regression networks for interval-valued data with outliers

This study reports a fixed-time tracking control problem for strict-feedback nonlinear systems with quantized inputs and actuator faults where the total number of faults is allowed to be infinite. By taking advantage of radial basis function neural networks (RBFNNs), unknown nonlinear function terms in the system dynamic model can be effectively approached. In addition, based on the sector property of quantization nonlinearities and the structure of the actuator fault model, novel adaptive estimations and innovative auxiliary design signals are constructed to compensate for the influence caused by actuator faults and quantized inputs properly in the fixed-time convergence settings. Then, rigorous theoretical analysis manifests that the proposed control scheme can make the output tracking error converge to a small neighborhood of the origin within a fixed time, and the upper bound of the setting time not only does not depend on initial states of the system but also can be preassigned by selecting parameters appropriately. Meanwhile, all the signals in the closed-loop system remain bounded. Finally, a numerical example and a practical example of a single-link manipulator are presented to demonstrate the effectiveness of the proposed control algorithm.

Shun-Feng Su

Papers

Immunity based genetic algorithm for solving quadratic assignment problem (QAP)

Direct Fuzzy Adaptive Regulation for High-Order Delayed Systems: A Lyapunov-Razumikhin Function Method.

A MergedFuzzy-Neural NetworkandItsApplication in Fuzzy-Neural Control

Robust interval support vector interval regression networks for interval-valued data with outliers

Neural Network-Based Fixed-Time Tracking Control for Input-Quantized Nonlinear Systems With Actuator Faults.