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This paper presents the Hierarchical Hopfield Neural Networks (HHNN). HHNN is a novel Hopfield Neural Network (HNN) approach. HHNN is composed of a hierarchy of self-sufficient HNNs, aiming to reduce the neural network structure and mitigate convergence problems. The HNNN composition depends on the applied problem. In this paper, the problem approached is the inter-domain routing for communication networks. Thus, the hierarchy of HNNs mimics the structure of communication networks (domains, nodes, and links). The proof of concept and the comparison between HNNN with the state-of-art HNN occurs using an implementation of them in the Java programming language. Besides, the performance analysis of the HHNN runs on a parallel hardware platform, using VHDL to develop it. The results have demonstrated a reduction of 93 . 75 % and 99 . 98 % in the number of neurons and connections to build the neural network, respectively. Furthermore, the mean time to achieve convergence of HHNN is rough 1 . 52 % of the total time needed by the current state-of-art HNN approach. It is also less susceptible to early convergence problems when used in communications networks with a large number of nodes. Last, but not least, the VHDL implementation shows that convergence time of HHNN is comparable to routing algorithms used in practical applications. [ABSTRACT FROM AUTHOR]

Physical topology design of optical networks is frequently accomplished by using evolutionary approaches. However, fitness evaluation for this type of problems is time consuming and the overall optimization process presents a huge execution time. In this paper we propose a new method that uses a multi-objective evolutionary approach to handle the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to minimize both the capital expenditure of the network and the network performance. Our method uses surrogate models to accelerate the fitness evaluation and a novel network generative model based on preferential attachment to generate the seeds for the evolutionary process. Our approach can provide high quality solutions with a very small execution time when compared to the previous approaches. In order to assess our proposal we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions for scenarios considering uniform and non-uniform traffic matrices. From our results, we obtained an evolutionary approach that presents better solutions than previous proposals for all analyzed scenarios. Our proposal presents an execution time that is up to 84% and 88% lower than the execution time needed by the previous approaches for uniform and non-uniform traffic, respectively. [ABSTRACT FROM AUTHOR]

Ensuring the reliable operation of diesel/Heavy fuel oil (HFO) engines in Thermoelectric Power Plants (TPPs) requires constant monitoring and control of operational parameters. Time series forecasting methods can predict physical system characteristics based on historical data, which can help regulate engine operational parameters. Despite the widespread use of these techniques in coal and natural gas powered TPPs, further examination of their application in large diesel/HFO engines in Brazilian TPPs operating under dispatch conditions is needed. This work investigated the fuel consumption generalization capacity of linear, nonlinear, and hybrid prediction models, both considering univariate and multivariate approaches, related to a TPP engine-driven generator in Pernambuco, Brazil. For multivariate model development, exogenous variables were selected based on change of causality overtime analysis, which sought not only to infer the relationship between signals but also to identify the influence regime of each causality during operations. The feature selection step initially identified a performance improvement when eight additional features were used. However, each feature causality throughout the operations indicated that only four signals helped significantly predict consumption in different ways. The exogenous variables introduction to ARIMA and NAR models resulted in significant improvements only to the nonlinear approach, NARX, which could recognize operational disturbances more quickly when compared to other analyzed models. In addition, the application of additive and multivariate hybrid models provided the ability to detect more complex variations and, at the same time, maintain model stability during full-load operation, benefiting from linear and nonlinear characteristics capturing ability related to the combined model. • Engines' FOC was analyzed based on univariate and multivariate prediction models. • Linear, nonlinear, and hybrid prediction models were used. • Exogenous variables were selected based on change of causality overtime analysis. • Exogenous variable's introduction to NAR resulted in significant improvements. • Hybrid prediction models detected complex variations and maintained model stability. [ABSTRACT FROM AUTHOR]