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The particulate matter PM10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models’ development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models’ performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models’ superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models’ efficiency to assist the hospital admissions management during high air pollution episodes.

Although the proportional integral derivative (PID) is a well-known control technique applied to many applications, it has performance limitations compared to nonlinear controllers. GAPID (Gaussian Adaptive PID) is a non-linear adaptive control technique that achieves considerably better performance by using optimization techniques to determine its nine parameters instead of deterministic methods. GAPID represents a multimodal problem, which opens up the possibility of having several distinct near-optimal solutions, which is a complex task to solve. The objective of this article is to examine the behavior of many optimization algorithms in solving this problem. Then, 10 variations of bio-inspired metaheuristic strategies based on Genetic Algorithms (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are selected to optimize the GAPID control of a Buck DC–DC converter. The computational results reveal that, in general, the variants implemented for PSO and DE presented the highest fitness, ranging from 0.9936 to 0.9947 on average, according to statistical analysis provided by Shapiro–Wilks, Kruskall–Wallis and Dunn–Sidak post-hoc tests, considering 95% of confidence level.

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

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O Brasil desempenha um papel importante na produção mundial de subprodutos gerados a partir da cana-de-açúcar, principalmente açúcar e etanol. O açúcar ́e uma das mais antigas commodities comercializadas pelo país. Realizar uma previsão adequada dos preços de tal produto tem impacto direto no sistema econômico, pois auxilia os gestores nos planejamentos estratégicos e minimiza os riscos futuros com uma avaliação mais precisa de suas tendências de mercado. Assim, o presente estudo tem como objetivo realizar a previsão do preço do açúcar, usando os modelos autorregressivo (AR), Perceptron de Multiplas Camadas (MLP), Máquinas de Aprendizado Extremo(ELM) e Redes Neurais com Estados de Eco (ESN). Os resultados experimentais evidenciaram que os modelos que apresentaram melhores desempenhos para um passo a frente nas bases CEPEA e NY No. 11, foram ELM e MLP, respectivamente.

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