pth moment exponential stability of stochastic fuzzy Cohen–Grossberg neural networks with discrete and distributed delays

In this paper, stochastic fuzzy Cohen–Grossberg neural networks with discrete and distributed delays are investigated. By using Lyapunov function and the Ito differential formula, some sufficient conditions for the pth moment exponential stability of such stochastic fuzzy Cohen–Grossberg neural networks with discrete and distributed delays are established. An example is given to illustrate the feasibility of our main theoretical findings. Finally, the paper ends with a brief conclusion. Methodology and achieved results is to be presented

Improved Results on H∞ State Estimation of Static Neural Networks with Time Delay

This paper studies the problem of ∞ state estimation for a class of delayed static neural networks. The purpose of the problem is to design a delay-dependent state estimator such that the dynamics of the error system is globally exponentially stable and a prescribed ∞ performance is guaranteed. Some improved delay-dependent conditions are established by constructing augmented Lyapunov-Krasovskii functionals (LKFs). The desired estimator gain matrix can be characterized in terms of the solution to LMIs (linear matrix inequalities). Numerical examples are provided to illustrate the effectiveness of the proposed method compared with some existing results

Robust ∞ Filtering for a Class of Complex Networks with Stochastic Packet Dropouts and Time Delays

The robust ∞ filtering problem is investigated for a class of complex network systems which has stochastic packet dropouts and time delays, combined with disturbance inputs. The packet dropout phenomenon occurs in a random way and the occurrence probability for each measurement output node is governed by an individual random variable. Besides, the time delay phenomenon is assumed to occur in a nonlinear vector-valued function. We aim to design a filter such that the estimation error converges to zero exponentially in the mean square, while the disturbance rejection attenuation is constrained to a given level by means of the ∞ performance index. By constructing the proper Lyapunov-Krasovskii functional, we acquire sufficient conditions to guarantee the stability of the state detection observer for the discrete systems, and the observer gain is also derived by solving linear matrix inequalities. Finally, an illustrative example is provided to show the usefulness and effectiveness of the proposed design method

Sliding intermittent control for BAM neural networks with delays

Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2013/615947 Open AccessThis paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM) neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Active Control Strategies for Chemical Sensors and Sensor Arrays

Chemical sensors are generally used as one-dimensional devices, where one measures the sensor’s response at a fixed setting, e.g., infrared absorption at a specific wavelength, or conductivity of a solid-state sensor at a specific operating temperature. In many cases, additional information can be extracted by modulating some internal property (e.g., temperature, voltage) of the sensor. However, this additional information comes at a cost (e.g., sensing times, power consumption), so offline optimization techniques (such as feature-subset selection) are commonly used to identify a subset of the most informative sensor tunings. An alternative to offline techniques is active sensing, where the sensor tunings are adapted in real-time based on the information obtained from previous measurements. Prior work in domains such as vision, robotics, and target tracking has shown that active sensing can schedule agile sensors to manage their sensing resources more efficiently than passive sensing, and also balance between sensing costs and performance. Inspired from the history of active sensing, in this dissertation, we developed active sensing algorithms that address three different computational problems in chemical sensing. First, we consider the problem of classification with a single tunable chemical sensor. We formulate the classification problem as a partially observable Markov decision process, and solve it with a myopic algorithm. At each step, the algorithm estimates the utility of each sensing configuration as the difference between expected reduction in Bayesian risk and sensing cost, and selects the configuration with maximum utility. We evaluated this approach on simulated Fabry-Perot interferometers (FPI), and experimentally validated on metal-oxide (MOX) sensors. Our results show that the active sensing method obtains better classification performance than passive sensing methods, and also is more robust to additive Gaussian noise in sensor measurements. Second, we consider the problem of estimating concentrations of the constituents in a gas mixture using a tunable sensor. We formulate this multicomponent-analysis problem as that of probabilistic state estimation, where each state represents a different concentration profile. We maintain a belief distribution that assigns a probability to each profile, and update the distribution by incorporating the latest sensor measurements. To select the sensor’s next operating configuration, we use a myopic algorithm that chooses the operating configuration expected to best reduce the uncertainty in the future belief distribution. We validated this approach on both simulated and real MOX sensors. The results again demonstrate improved estimation performance and robustness to noise. Lastly, we present an algorithm that extends active sensing to sensor arrays. This algorithm borrows concepts from feature subset selection to enable an array of tunable sensors operate collaboratively for the classification of gas samples. The algorithm constructs an optimized action vector at each sensing step, which contains separate operating configurations for each sensor in the array. When dealing with sensor arrays, one needs to account for the correlation among sensors. To this end, we developed two objective functions: weighted Fisher scores, and dynamic mutual information, which can quantify the discriminatory information and redundancy of a given action vector with respect to the measurements already acquired. Once again, we validated the approach on simulated FPI arrays and experimentally tested it on an array of MOX sensors. The results show improved classification performance and robustness to additive noise