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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]

Accuracy, robustness, and minimality are fundamental issues in system-level design. Such properties are generally associated with constraints limiting the feasible model space. The paper focuses on the optimal selection of feedforward neural networks under the accuracy, robustness, and minimality constraints. Model selection, with respect to accuracy, can be carried out within the theoretical framework delineated by the final prediction error (FPE), generalization error estimate (GEN), general predicion error (GPE) and network information criterion (NIC) or cross-validation-based techniques. Robustness is an appealing feature since a robust application provides a graceful degradation in performance once affected by perturbations in its structural parameters (e.g., associated with faults or finite precision representations). Minimality is related to model selection and attempts to reduce the computational load of the solution (with also silicon area and power consumption reduction in a digital implementation). A novel sensitivity analysis derived by the FPE selection criterion is suggested in the paper to quantify the relationship between performance loss and robustness; based on the definition of weak and acute perturbations, we introduce two criteria for estimating the robustness degree of a neural network. Finally, by ranking the features of the obtained models we identify the best constrained neural network. Index Terms--Acute perturbations, constrained model selection, feedforward neural networks, robustness, sensitivity, system-level design.

The paper quantifies the degradation in performance of a linear model induced by perturbations affecting its identified parameters. We extend sensitivity analyses available in the literature, by considering a generalization-based figure of merit instead of the inaccurate training one. Effective off-line techniques reducing the impact of perturbations on generalization performance are introduced to improve the robustness of the model. It is shown that further robustness can be achieved by optimally redistributing the information content of the given model over topologically more complex linear models of neural network type. Despite the additional robustness achievable, it is shown that the price we have to pay might be too high and the additional resources would be better used to implement a n-ary modular redundancy scheme. Index Terms--Linear computation, linear neural networks, perturbation analysis, robustness, sensitivity.

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]

The problem of sensitivity to errors in artificial neural networks is discussed here considering an abstract model of the network and the errors that can affect a neuron's computation. Feed-forward multi-layered networks are considered; the performance taken into account with respect to error sensitivity is their classification capacity. The final aim is evaluation of the probability that a single neuron's error will affect both its own classification capacity and that of the whole network. A geometrical representation of the neural computation is adopted as the basis for such evaluation. Probability of error propagation is evaluated with respect to the single neuron's output as well as to the complete network's output. The information derived is used to evaluate, for a specific digital network architecture, the most critical sections of the implementation as far as reliability is concerned and thus to point out candidates for ad-hoc fault-tolerance policies.

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]

Evolutionary multiobjective optimization in dynamic environments is a challenging task, as it requires the optimization algorithm converging to a time-variant Pareto optimal front. This paper proposes a dynamic multiobjective optimization algorithm which utilizes an inverse model set to guide the search toward promising decision regions. In order to reduce the number of fitness evalutions for change detection purpose, a two-stage change detection test is proposed which uses the inverse model set to check potential changes in the objective function landscape. Both static and dynamic multiobjective benchmark optimization problems have been considered to evaluate the performance of the proposed algorithm. Experimental results show that the improvement in optimization performance is achievable when the proposed inverse model set is adopted. [ABSTRACT FROM PUBLISHER]

We propose incremental least squares density difference (LSDD) change detection method, an incremental test to detect changes in stationarity based on the difference between the unknown prechange and the post-change probability density functions (pdfs). The method is computationally light and, hence, adequate to process continuous datastreams, as those emerging from the Internet of Things and the big data framework. The incremental change detection test operates on two nonoverlapping data windows to estimate the LSDD between the two pdfs. We construct a theoretical framework that shows how the distribution of LSDD values follows a linear combination of \chi ^2 distributions and provides thresholds to control false positive rates. The proposed test can operate online, with needed estimates and thresholds computed incrementally as fresh samples come. Comprehensive experiments validate the effectiveness of the test both in detecting abrupt and drift types of changes. [ABSTRACT FROM AUTHOR]

Advances in embedded systems and communication technologies--along with the availability of low-cost sensors--have led to a pervasive presence of cyber-physical systems. However, several intelligent functionalities that are necessary to meet user and application demands are missing from current solutions. A homogeneous and integrated framework supporting intelligent mechanisms can fill this gap. [ABSTRACT FROM PUBLISHER]

The authors discuss the concept of cloud computing and its application for networks, focusing on the development of a new Internet of Things (IoT) paradigm aimed at closing the gap of computing, storage and services with Iot objects and reduce energy consumption and communication bandwidth. It mentions that the proposed fog computing (FC)-IoT solution features intelligent and autonomous IoT objects with an FC and fog networking approach.

In recent years, Radio frequency (RF) sensor networks have been used to localize people indoor without requiring them to wear invasive electronic devices. These wireless mesh networks, formed by low-power radio transceivers, continuously measure the received signal strength (RSS) of the links. Radio Tomographic Imaging (RTI) is a technique that generates, starting from these RSS measurements, 2D images of the change in the electromagnetic field inside the area covered by the radio transceivers to spot the presence and movements of animates (e.g., people, large animals) or large metallic objects (e.g., cars). Here, we present a RTI system for localizing and tracking people outdoors. Differently than in indoor environments where the RSS does not change significantly with time unless people are found in the monitored area, the outdoor RSS signal is time-variant, e.g., due to rainfall or wind-driven foliage. We present a novel outdoor RTI method that, despite the nonstationary noise introduced in the RSS data by the environment, achieves high localization accuracy and dramatically reduces the energy consumption of the sensing units. Experimental results demonstrate that the system accurately detects and tracks a person in real-time in a large forested area under varying environmental conditions, significantly reducing false positives, localization error and energy consumption compared to state-of-the-art RTI methods. [ABSTRACT FROM PUBLISHER]

The lack of clearly noticeable forerunners, the need to acquire large amounts of data coming from sensors sampled at middle/high rates, and the potentially catastrophic effects of the physical phenomenon under monitoring make rock-collapse forecasting a challenging and valuable environmental monitoring application. In this paper, we describe a rock-collapse forecasting system based on a hybrid wireless–wired architecture, where a set of acquisition units is connected through a fieldbus to a base station, which collects and wirelessly transmits acquired measurements to a remote control room. The main features of the proposed rock-collapse forecasting system are the ability to process data sampled at high-frequency rates locally and in real time, the run-time remote reconfigurability of the forecasting application, and the possibility to distribute intelligent processing through the system layers to balance energy consumption and the application performance. Five instances of the proposed system have been deployed along the Swiss–Italian Alps. [ABSTRACT FROM PUBLISHER]