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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]

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]

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]

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]

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 article considers the tracking control problem of an underwater vehicle used in the exploitation of combustible gas deposits at great sea depths. The vehicle is subjected to different load configurations that introduce considerable variations of its mass and inertial parameters. In this work it is assumed that the possible vehicle configurations are known, but the time instants when the changes occur and the new vehicle configuration following the change are unknown. A neural network-based switching control is proposed for the considered mode-switch process. This solution simplifies the control scheme implementation and reduces the control signal chattering. [ABSTRACT FROM AUTHOR]

This article presents a new linear matrix inequality-based approach to an H∞ output feedback control problem of master—slave synchronisation of artificial neural networks with uncertain time-delay, which can exhibit chaotic behaviour. The uncertain time-delay is considered as a composition of a nominal positive value subject to a time-varying perturbation. The methodology to be employed is based on the selection of a new discretised Lyapunov-Krasovskii functional (LKF) with two parts: the first is related to the nominal delay, and the second one is related to the time-varying perturbation. Extra manipulations allows us to introduce free matrices decoupling the LKF matrices from the system matrices, turning to obtain a control design condition easier. Finally, an insightful numeric simulation will be proposed to show the effectiveness of this kind of methodology to the problem of synchronising coupled chaotic delayed artificial neural networks. Besides, based on the information transmission via control principle, two information transmission experiments are performed as a possible application or/and an index to measure the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The literature reports many scientific works on the use of artificial intelligence techniques such as neural networks or fuzzy logic to predict surface roughness. This article aims at introducing Bayesian network-based classifiers to predict surface roughness (Ra) in high-speed machining. These models are appropriate as prediction techniques because the non-linearity of the machining process demands robust and reliable algorithms to deal with all the invisible trends present when a work piece is machining. The experimental test obtained from a high-speed milling contouring process analysed the indicator of goodness using the Naive Bayes and the Tree-Augmented Network algorithms. Up to 81.2% accuracy was achieved in the Ra classification results. Therefore, we envisage that Bayesian network-based classifiers may become a powerful and flexible tool in high-speed machining. [ABSTRACT FROM AUTHOR]

This article presents a soft-computing-based data mining technique that addresses methodology aspects on extracting classification rules characterized by ellipsoidal regions in feature space. Self-organizing mapping and statistical techniques are employed to initialize the rules. A regularization model embedding some information on recognition rate and generalization ability is presented for refining the initial rules. Rule optimization is implemented for each individual rule using an evolutionary strategy. To generate rules for patterns with low probability of occurrence but considerable conceptual importance, a multilayer structure of rule generation and use is proposed. Simulation results are carried out by three benchmark data sets, and compared with other data mining tools and classifiers, such as decision trees, BRAINNE (Building Representations of Artificial Intelligence (AI) using Neural Networks), support vector machine, and neural networks. Our technique demonstrates its power and potential for real-world applications. [ABSTRACT FROM AUTHOR]

Data in many real world applications are high-dimensional and learning algorithms like neural networks may have problems in handling high-dimensional data. However, the ‘intrinsic dimension (ID)’ is often much less than the original dimension of the data. Here, we use fractal based methods to estimate the ID and show that a nonlinear projection method called curvilinear component analysis (CCA) can effectively reduce the original dimension to the ID. We apply this approach for dimensionality reduction of the face images data and use neural network classifiers for gender classification. [ABSTRACT FROM AUTHOR]