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Here, this article reports about the design of a soft sensor (SS) able to monitor the hazardous gases in industrial plants. The SS is designed to estimate the gas concentrations by means of the measurements coming from an array of sensors, avoiding at the same time the humidity and temperature influence on array outputs. The SS has been designed with a data-driven approach, using a set of experimental data acquired in a laboratory. The design methodologies of two different SSs are compared, with the aim of obtaining both the good performance and a low computational complexity. As a first approach, a principal component analysis (PCA) has been performed to exploit the high correlation among some of the measures coming from the sensor array. A classical multilayer perceptron neural network is then trained to estimate the relationships between the PCA outputs and the gas concentrations. As a second approach, a deep belief network (DBN) has been considered. The data here reported show a good accuracy in the evaluation of several gas concentrations, even in the presence of noised measurements, allowing an efficient risk warning. Even if both the methods gave a similar performance, a lower number of model parameters and a lower noise sensitivity are obtained when using DBNs.

This paper introduces a soft sensor (SS) for the estimation of the deflection of a polymeric mechanical actuator. The actuator is based on ionic polymer-metal composites (IPMCs). Applications of IPMCs have been proposed in fields such as robotics, surgery, and aerospace, to mention the most interesting ones. In such application fields, both the complexity and the size of the actuating system are of chief importance. An SS can be, therefore, preferred to hardware measuring the actuator output, for estimating the actuator motion. Also, low-order models are of interest to limit the computational load, which can be a constraint in real-time applications. To this aim, several data-driven nonlinear finite-impulse response (NFIR) models have been investigated. Data, used for the model identification, have been acquired, in controlled environmental conditions, by using swept signals as the input to the IPMC actuator. Linear and nonlinear models, based on principal component analysis, shallow, and deep neural networks (NNs), have been investigated, for different model orders. The best results have been obtained by an SS based on a fifth-order NFIR model, implemented by a deep belief NN.

This paper proposes an experimental analysis on the convergence of evolutionary algorithms (EAs). The effect of introducing chaotic sequences instead of random ones during all the phases of the evolution process is investigated. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences. Several numerical examples are reported in order to compare the performance of the EA using random and chaotic generators as regards to both the results and the convergence speed. The results obtained show that some chaotic sequences are always able to increase the value of some measured algorithm-performance indexes with respect to random sequences. Moreover, it is shown that EAs can be extremely sensitive to different RNGs. Some t-tests were performed to confirm the improvements introduced by the proposed strategy.