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This paper introduces a new class of discrete-time two-dimensional (2-D) switched systems with multiplicative noise. Firstly, we extend the definition of the asymptotic stability and establish a sufficient stochastically stability condition for this new model under arbitrary switching signal. Then, by introducing the average dwell time into this new model and combining with the Lyapunov function, we investigate the extended stochastic exponential stability of the 2-D switched systems with multiplicative noise for the restricted switching case. Moreover, some remarks and discussions are given to illustrate the significance of obtained results, which generalise and comprise some previous results the literature. Finally, two examples are provided to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the attitude tracking problem for the rigid body with model uncertainties and external disturbances is investigated in a coordinate-free way. A continuous adaptive-gain second-order sliding mode controller is designed to ensure the establishment of a real second-order sliding mode in finite time, and then the predefined nonsingular fast sliding surface further implies finite-time stability of the attitude error vector and the angular velocity error vector. The key feature of the proposed controller is that it does not require the knowledge of the boundary of the disturbance gradient. A rigorous mathematical proof for the stability of the control system is derived by using the Lyapunov function technique. Finally, simulation comparisons illustrate the effectiveness and robustness of the controller. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of an adaptive fuzzy dynamic surface control for uncertain nonlinear pure-feedback systems with input and state constraints using a set of noisy measurements. The design approach is described as follows. The nonlinear uncertainties are approximated by using the fuzzy logic systems at the first stage, secondly the adaptive fuzzy dynamic surface control is introduced to remove the problem of the explosion of complexity for the derivation of the adaptive fuzzy backstepping control, thirdly a new saturation function for state constraints is proposed to design the controllers based on the Lyapunov function, fourthly the number of the adjustable parameters is reduced by using the simplified extended single input rule modules, and finally the weighted least squares estimator to take the estimates for the un-measurable states and the adjustable parameters is in a simplified structure designed. The proposed approach provides effective system performance in the simulation experiment. [ABSTRACT FROM AUTHOR]

This paper proposes an exact order reduction approach for the multidimensional (n-D) Fornasini-Marchesini (F-M) model by making use of the common eigenvector. Specifically, by introducing the concept of common eigenvectors, sufficient conditions of exact order reductions are developed for an n-D F-M model, which are able to simultaneously deal with n eigenvalues of the system matrices of the n-D F-M model. The obtained results reveal, for the first time, the internal connection between the multiple eigenvalues of the system matrices and the reducibility of the considered n-D F-M model. Then, a corresponding algorithm is proposed to exactly reduce the order of an n-D F-M model as much as possible. Examples are given to illustrate the details as well as the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the leader-following fixed-time output feedback consensus problem for second-order multi-agent systems with input saturation. By combing fixed-time control technique and bi-limit homogeneous systems theory, a class of bounded fixed-time consensus protocols are developed for leader-following multi-agent systems. The protocol design is divided into two parts. First, when all the state information of the followers are measurable, a state feedback consensus protocol is designed to achieve fixed-time consensus. Then, when the velocity information is unmeasurable, an observer-based fixed-time consensus protocol is proposed. With the help of Lyapunov stability theorem and the property of a homogeneous function, it is theoretically shown that the states of all followers can track that of the leader in fixed-time in the presence of input saturation. Finally, numerical simulation is carried out to illustrate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

This paper extends research in the area of biologically inspired, discrete dynamical basis networks (DDBNs). While similar to locally recurrent globally feed forward (LRGF) networks (Tsoi and Back 1994). DDBNs operate at a lower level of abstraction and were inspired by research in the areas of Control Systems. Artificial Intelligence and Neurobiology, As described previously, DDBNs can approximate data sequences and consist of networks of simple, bounded mathematical operators (Jones and Olmsted 2003). This paper examines the characteristics of genetically designed DDBNs and compares them with tree-based genetic programs (TBGPs), biological neural networks, and backpropagation neural networks (NNs). Experimental evidence indicates that DDBNs are capable of computing simple logic functions in addition to approximating data sequences. [ABSTRACT FROM AUTHOR]

In this paper, a new direct self-repairing control scheme is developed for a helicopter flight control system with unknown actuator faults and external disturbance. The design of multi-model-based adaptive control is used to accommodate the faulty system under different fault conditions. By appropriate switching based on quantum information technique, the system can be converted to the best model and the corresponding controller. Asymptotic model following performance and system stability is guaranteed. A disturbance observer is introduced to observe the disturbance of the system, which can produce corresponding control signals according to the disturbance. The results including a numerical simulation and a semi-physical verification demonstrate the effectiveness of the proposed self-repairing control approach for the helicopter flight control system. [ABSTRACT FROM PUBLISHER]

This paper considers the adaptive containment control problem of second-order multi-agent systems with inherent nonlinear dynamics. In particular, the leaders’ control inputs are nonzero, bounded, and not available to any follower. Based on the relative states among neighbouring agents, a discontinuous adaptive protocol is first proposed to ensure that the containment errors of each follower converge to zero asymptotically, i.e. the states of the followers asymptotically converge to the convex hull spanned by those of the leaders. To eliminate the chattering effect caused by the discontinuous protocol, a continuous adaptive protocol is further designed based on the boundary layer technique and the σ-modification technique. Numerical examples are provided to demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of the crew exploration vehicle (CEV) attitude control. CEVs are NASA's next-generation human spaceflight vehicles, and they use reaction control system (RCS) jet engines for attitude adjustment, which calls for control algorithms for firing the small propulsion engines mounted on vehicles. In this work, the resultant CEV dynamics combines both actuation and attitude dynamics. Therefore, it is highly nonlinear and even coupled with significant uncertainties. To cope with this situation, a neural–immunology/memory network is proposed. It is inspired by the human memory and immune systems. The control network does not rely on precise system dynamics information. Furthermore, the overall control scheme has a simple structure and demands much less computation as compared with most existing methods, making it attractive for real-time implementation. The effectiveness of this approach is also verified via simulation. [ABSTRACT FROM PUBLISHER]

This study aims at development of practical stability estimation method for PFM-type pulsed neural networks. We already derived a graphical analysis method for a single-unit PFM-type neural network. In this paper, we extend this method so that it can deal with stability estimation of networks with multi-unit PFM-type pulsed neurons by using the concept of Generalised Nyquist Stability Criterion . We also illustrate numerical examples that show the extended method is not excessively conservative but still effectively estimates the non-existence of limit cycles. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have been used to optimise the performance of neural network models before. This paper uses a hybrid approach by permanently attaching a genetic algorithm (GA) to a hierarchical clusterer to investigate appropriate parameter values for producing specific tree-shaped representations for some gene sequence data. It addresses a particular problem where the size of the data set makes the direct use of a GA too time consuming. We show by using a data set nearly two orders of magnitude smaller in the GA investigation that the results can be usefully translated across to the real, much larger data sets. The data sets in question are gene sequences and the aim of the analysis was to cluster short sub-sequences that could represent binding sites that regulate the expression of genes. [ABSTRACT FROM AUTHOR]

The paper describes a soft computing agent approach to intelligent control of remote learning. It presents a special type of decision making soft computing agent applied to the selection of appropriate groups for definition of individual remote learning purposes. Soft computing agents are used to perform two tasks: optimal partitioning of distributed data bases in accordance with a grade of ability, and coordination in distributed environment for remote group definition. The first task is realized via two-hierarchical fuzzy system for optimisation of assessment parameters. The second is achieved by means of mobile intelligent agents for transmission, coordination, activation, and receiving decisions from/to remote learning nodes. As a final decision the node soliciting optimal group choice obtains information for group distributions over the whole system. This approach is applied in some cases to disabled people suffering from dyslexia. Dyslexia is defined as a learning disability by four psychologically obtained factors that present the grade of learning deficiency. They are preliminary provided for better understanding and formalisation of remote learning process applied to disable people. [ABSTRACT FROM AUTHOR]

Prediction by regression plays an important role in intelligent systems. To construct a regression model for fuzzy numbers, this paper decomposes a fuzzy number into two parts: the position and fuzziness. The former is represented by the elements with membership value 1 and the latter by the entropy of the fuzzy number; both have crisp values. The conventional regression analysis is applied to find the relationship between the position (and entropy) of the fuzzy response variable and that of the fuzzy explanatory variables. Given a set of fuzzy explanatory variables, the position and entropy of the estimated fuzzy responses are calculated from the regression model. Via the one-to-one correspondence between a fuzzy number and its entropy, the estimated fuzzy response is obtained. [ABSTRACT FROM AUTHOR]

This paper shows hove' the solution of the standard predictive control problem can be recast as a continuous function of the state, the reference signal, the noise and the disturbances. and hence can be approximated arbitrarily closely by a feed-forward neural network. The existence of such a continuous mapping eliminates the need for linear independency of the active constraints, and therefore the resulting analytic constrained predictive controller will combine constraint handling with speed while being applicable to fast and complex control systems with many constraints. The effectiveness of the proposed controller design methodology is shown for a simulation example of an elevator model and for a real-time laboratory inverted pendulum system. [ABSTRACT FROM AUTHOR]

Forecasting of warranty performance helps car engineers to fine-tune their strategies for warranty cost reduction. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at a certain future time, but also future MIS values. However, the ‘maturing data’ phenomenon that causes a warranty performance measure at specific MIS values to change with time make such forecasting challenging. Although dynamic linear models have been used for forecasting warranty performance, the focus mainly has been to utilize previous-model-year vehicle data for the analysis. In this paper, we apply a neural network model to forecast year-end warranty performance in the presence of the ‘maturing data’ phenomenon. We use a special type of neural network, viz. radial basis function (RBF), and optimize its parameters by minimizing training and testing errors through planned experimentation. This application shows the effectiveness of RBF neural networks to forecast warranty performance in the presence of the ‘maturing data’ phenomenon. [ABSTRACT FROM AUTHOR]

Foreign exchange trading has emerged recently as a significant activity in many countries. As with most forms of trading the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. A major issue for traders in the deregulated Foreign Exchange Market is when to sell and when to buy a particular currency in order to maximize profit. This paper presents novel trading strategies based on the machine learning methods of genetic algorithms and reinforcement learning. [ABSTRACT FROM AUTHOR]

This paper presents a decision support system (DSS) that enables logistics planners of a well-known bank to design intra-city safe delivery routes, using an intelligent metaheuristic method and exploiting risk methodologies and spatial data information. The DSS routes minimize the probability of successful vehicle robbery at a certain point of the road network and satisfy all operational constraints of the distribution problem examined. The proposed DSS was implemented to an actual banking environment and the results obtained by this real-life application are reported. [ABSTRACT FROM AUTHOR]

This paper introduces an approach for modelling and designing multi-agent control architectures for agile manufacturing using a generic formalism based on a system-theoretic discrete event approach. To describe the details of the modelling strategy. we apply the proposed approach to a multi-agent network for job flow control in a manufacturing plant. Two interacting types of autonomous controllers. Part Agents and Machine Agents, are in charge of controlling the part flow and the machine processing sequences. Both type of agents are first modelled as atomic discrete event systems and subsequently integrated m the model of the entire network of autonomous controllers. To improve the performance of the network of agents, we introduce a mechanism based on evolutionary algorithms adapting the agents' decision laws that are encapsulated in agents' states. Through network simulation, the algorithm continuously searches for effective decision laws, consequently adapting agent's behaviour to the current operational conditions of the manufacturing floor. Simulation results show the potentialities of the approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose an Adaptive Neuro-Fuzzy Network (ANFN) to deal with forecasting problems. The ANFN model is inherently a modified Takagi-Sugeno-Kang-type fuzzy-rule-based model possessing a neural network's learning ability. We propose a hybrid learning algorithm which combines the Genetic Algorithm (GA) and the Least-Squares Estimate (LSE) method to construct the ANFN model. The GA is used to tune membership functions at the precondition part of fuzzy rules, while the LSE method is used to tune parameters at the consequent part of fuzzy rules. Simulations demonstrate that the proposed ANFN model has a good predictive capability. [ABSTRACT FROM AUTHOR]

This paper considers the problem of controlling partially uncertain , possibly unstable and non-minimum phase linear plants submitted to a control saturation constraint. It seeks both plant l ∞ -stabilization and output tracking. It proposes a direct adaptive controller and shows that if the closed loop poles are placed in a specific region, then the desired stability and tracking objectives are achieved. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive fuzzy neural controller (AFNC) suitable for modelling and control of MIMO non-linear dynamic systems. The proposed AFNC has the following salient features: (1) fuzzy neural control rules can be generated or deleted dynamically and automatically; (2) uncertain MIMO non-linear systems can be adaptively modelled on line; (3) adaptation and learning speed is fast; (4) expert knowledge can be easily incorporated into the system; (5) the structure and parameters of the AFNC can be self-adaptive in the presence of uncertainties to maintain a high control performance; and (6) the asymptotical stability of the system is established using the Lyapunov approach. Simulation studies on a two-link robot manipulator show that the performance of the proposed controller is better than that of some existing fuzzy/neural methods. [ABSTRACT FROM AUTHOR]

With the advent of Internet technology, it is now feasible to provide effective and efficient helpdesk service over the global Internet to meet customers' requirements and satisfaction. In this research, we have designed and developed a Web-based intelligent helpdesk-support environment, WebHotLine, to support the customer service centre of a large multinational corporation in the electronics industry. The paper describes the basic architecture of the environment that supports the major functions of Web-based fault information retrieval, online multilingual translation capability, different operating modes of video-conferencing for enhanced support and direct intelligent fault diagnosis by customers or customer support engineers. As a result, WebHotLine helps to save cost in eliminating the expensive overseas telephone charges, reduction in machine down time and number of on-site visits by service engineers as in traditional helpdesk environment. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to developing an intelligent agile design system for rolling bearings based on artificial intelligence (AI), Internet and Web technologies and expertise. The underlying philosophy of the approach is to use AI technology and Web-based design support systems as smart tools from which design customers can rapidly and responsively access the systems' built-in design expertise. The approach is described in detail with a novel AI model and system implementation issues. The major issues in implementing the approach are discussed with particular reference to using AI technologies, network programming, client-server technology and open computing of bearing design and manufacturing requirements. [ABSTRACT FROM AUTHOR]

This paper presents the processes of knowledge acquisition and ontology development for structuring the knowledge base of an expert system. Ontological engineering is a process that facilitates construction of the knowledge base of an intelligent system. Ontology is the study of the organization and classification of knowledge. Ontological engineering in artificial intelligence has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledgeintensive problems and it supports knowledge sharing and reuse. To illustrate the process of conceptual modelling using the Inferential Modelling Technique as a basis for ontology construction, the tool and processes are applied to build an expert system in the domain of monitoring of a petroleum-production facility. [ABSTRACT FROM AUTHOR]

Data pre-processing forms an important element of pattern recognition procedures for mechanical and structural damage detection. This paper discusses feature extraction and selection procedures. The discussion is focused on time-frequency and time-scale analysis. Case studies of damage detection exercises are presented for illustration. [ABSTRACT FROM AUTHOR]

Artificial intelligence focuses on the question of how to design systems to exhibit intelligent behaviour in complex environments. Complex global behaviours can emerge from simple systems acting in a complex environment; however, this emergence requires that the systems' internal structure reflects essential structures in the environment This paper examines the algebraic structure of a system's actions. We find that these actions often possess a self-similar local neighbourhood structure that permits analysis and synthesis to be performed locally and yet produces global intelligent behaviours. A procedure for finding this local structure is presented and illustrated with examples. [ABSTRACT FROM AUTHOR]