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In this article, an event‐triggered robust control (ETRC) method is investigated for multi‐player nonzero‐sum games of continuous‐time input constrained nonlinear systems with mismatched uncertainties. By constructing an auxiliary system and designing an appropriate value function, the robust control problem of input constrained nonlinear systems is transformed into an optimal regulation problem. Then, a critic neural network (NN) is adopted to approximate the value function of each player for solving the event‐triggered coupled Hamilton–Jacobi equation and obtaining control laws. Based on a designed event‐triggering condition, control laws are updated when events occur only. Thus, both computational burden and communication bandwidth are reduced. We prove that the weight approximation errors of critic NNs and the closed‐loop uncertain multi‐player system states are all uniformly ultimately bounded thanks to the Lyapunov's direct method. Finally, two examples are provided to demonstrate the effectiveness of the developed ETRC method. [ABSTRACT FROM AUTHOR]

Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to polynomial ones, provides a more flexible frequency response, is more robust to noise, and better captures the global graph structure. We propose a graph neural network implementation of the ARMA filter with a recursive and distributed formulation, obtaining a convolutional layer that is efficient to train, localized in the node space, and can be transferred to new graphs at test time. We perform a spectral analysis to study the filtering effect of the proposed ARMA layer and report experiments on four downstream tasks: semi-supervised node classification, graph signal classification, graph classification, and graph regression. Results show that the proposed ARMA layer brings significant improvements over graph neural networks based on polynomial filters. [ABSTRACT FROM AUTHOR]

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed, k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications. Index Terms--Intelligent systems, learning systems, neural networks, pattern classification.

The success of wireless sensor networks and their pervasive use is somehow constrained by energy supply which, generally provided by batteries, is a finite resource. Energy harvesting mechanisms must hence be taken into account to grant a long time operational life, with solar energy being the most interesting one in outdoor deployments due to its relatively high power density. In this paper we propose a low-power maximum power point tracker (MPPT) circuit specifically designed for wireless sensor nodes (hence effective, flexible, low cost and power-aware), i.e., a power transferring circuit for optimally conveying solar energy into rechargeable batteries even in not optimal weather conditions. High efficiency is granted by an ad hoc adaptive algorithm which, by keeping the MPPT electronics in its optimal working point, maximizes energy transfer from the solar cell to the batteries. The suggested implementation is particularly effective in critical weather conditions where traditional solutions do not work and is characterized by a flexible enough design for immediately hosting, in a plug in fashion, different solar panels and battery typologies. Index Terms--Adaptive algorithms, maximum power point tracker (MPPT) circuits, power converters, solar energy harvesting, wireless sensor networks.

The stationarity requirement for the process generating the data is a common assumption in classifiers' design. When such hypothesis does not hold, e.g., in applications affected by aging effects, drifts, deviations, and faults, classifiers must react just in time, i.e., exactly when needed, to track the process evolution. The first step in designing effective just-in-time classifiers requires detection of the temporal instant associated with the process change, and the second one needs an update of the knowledge base used by the classification system to track the process evolution. This paper addresses the change detection aspect leaving the design of just-in-time adaptive classification systems to a companion paper. Two completely automatic tests for detecting nonstationarity phenomena are suggested, which neither require a pr/or/information nor assumptions about the process generating the data. In particular, an effective computational intelligence-inspired test is provided to deal with multidimensional situations, a scenario where traditional change detection methods are generally not applicable or scarcely effective. Index Terms--Intelligent systems, learning systems, neural networks, pattern classification.

Temporal and spatial locality of the inputs, i.e., the property allowing a classifier to receive the same samples over time---or samples belonging to a neighborhood--with high probability, can be translated into the design of embedded classifiers. The outcome is a computational complexity and power aware design particularly suitable for implementation. A classifier based on the gated-parallel family has been found particularly suitable for exploiting locality properties: Subclassifiers are generally small, independent each other, and controlled by a master-enabling module granting that only a subclassifier is active at a time, the others being switched off. By exploiting locality properties we obtain classifiers with accuracy comparable with the ones designed without integrating locality but gaining a significant reduction in computational complexity and power consumption. Index Terms--Application-level design, classifier design, embedded systems, gated-parallel classifiers, power-aware design.

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non‐linear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different—also traditional—architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting, by contrasting deep learning architectures on short‐term forecast (one‐day‐ahead prediction). Specifically, the focus is on feedforward and recurrent neural networks, sequence‐to‐sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one. [ABSTRACT FROM AUTHOR]

In recent years, the artificial intelligence community has seen a continuous interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks, we have the Reservoir Computing (RC) paradigm characterized by conceptual simplicity and a fast training scheme. Yet, the guiding principles under which RC operates are only partially understood. In this work, we analyze the role played by Generalized Synchronization (GS) when training a RC to solve a generic task. In particular, we show how GS allows the reservoir to correctly encode the system generating the input signal into its dynamics. We also discuss necessary and sufficient conditions for the learning to be feasible in this approach. Moreover, we explore the role that ergodicity plays in this process, showing how its presence allows the learning outcome to apply to multiple input trajectories. Finally, we show that satisfaction of the GS can be measured by means of the mutual false nearest neighbors index, which makes effective to practitioners theoretical derivations. [ABSTRACT FROM AUTHOR]

Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to support a real-time execution of the considered DL model at the IoT unit level, DL solutions must be designed having in mind constraints on memory and processing capability exposed by the chosen IoT technology. In this article, we introduce a design methodology aiming at allocating the execution of Convolutional Neural Networks (CNNs) on a distributed IoT application. Such a methodology is formalized as an optimization problem where the latency between the data-gathering phase and the subsequent decision-making one is minimized, within the given constraints on memory and processing load at the units level. The methodology supports multiple sources of data as well as multiple CNNs in execution on the same IoT system allowing the design of CNN-based applications demanding autonomy, low decision-latency, and high Quality-of-Service. [ABSTRACT FROM AUTHOR]

This article develops a novel sliding-mode surface (SMS)-based approximate optimal control scheme for a large class of nonlinear systems affected by unknown mismatched perturbations. The observer-based perturbation estimation procedure is employed to establish the online updated value function. The solution to the Hamilton–Jacobi–Bellman equation is approximated by an SMS-based critic neural network whose weights error dynamics is designed to be asymptotically stable by nested update laws. The sliding-mode control strategy is combined with the approximate optimal control design procedure to obtain a faster control action. The stability is proved based on the Lyapunov’s direct method. The simulation results show the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

Given a finite sequence of graphs, e.g. coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process that generated such graphs. We consider a general family of attributed graphs for which both topology (vertices and edges) and associated attributes are allowed to change over time, without violating the stationarity hypothesis. Novel Change-Point Methods (CPMs) are proposed that map graphs onto vectors, apply a suitable statistical test in vector space and detect changes –if any– according to a user-defined confidence level; an estimate for the change point is provided as well. In particular, we propose two multivariate CPMs: one designed to detect shifts in the mean, the other to address more complex changes affecting the distribution. We ground our methods on theoretical results that show how the inference in the numerical vector space is related to the one in graph domain, and vice-versa. We also extend the methodology to handle multiple changes occurring in a single sequence. Results show the effectiveness of what proposed in relevant application scenarios. [ABSTRACT FROM AUTHOR]