Multilayer Perceptron Neural Model Research Articles

Multilayer Perceptron Neural Model for Particle Identification in MPD

Multilayer Perceptron Neural Model for Particle Identification in MPD

An exergetically-sustainable operational condition of a photo-biohydrogen production system optimized using conventional and innovative fuzzy techniques

The aim of the present study was to perform an exergy-based multi-objective fuzzy optimization of a continuous photobioreactor applied for biohydrogen production from syngas via the water-gas shift reaction by Rhodospirillum rubrum. For this purpose, the conventional and innovative fuzzy optimization techniques coupled with multilayer perceptron (MLP) neural model to optimize the main exergetic performance parameters of the photobioreactor. The MLP neural model was applied to correlate three dependent variables (rational and process exergy efficiencies and normalized exergy destruction) with two independent variables (syngas flow rate and agitation speed). The developed MLP model was then interfaced with three different multi-objective fuzzy optimization systems with independent, interdependent, and locally modified interdependent objectives. The optimization process was aimed at maximizing the rational exergy and process efficiencies, while minimizing the normalized exergy destruction, simultaneously. Generally, the innovative locally modified interdependent objectives fuzzy system showed a better optimization capabilities compared with the other two fuzzy systems. Accordingly, the optimal syngas photo-fermentation for biohydrogen production in the continuous bioreactor corresponded to the agitation speed of 383.34 rpm and syngas flow rate of 13.35 mL/min in order to achieve the normalized exergy destruction of 1.56, rational exergy efficiency of 85.65%, and process exergy efficiency of 21.66%.

Predictive Method of Networked Control of Industrial Automation Systems

A New method for real-time prediction of uncertain network transmission time delay and the closed-loop control method through the network manufacturing and industrial plant are introduced. Put forward delay prediction method is based on the multilayer perceptron neural model. To reduce the amount of the first layer of neurons network, thus reducing the computational burden a real-time implementation, a method for the determination of Markov delay sequence order. Used to predict delay and a zero order equivalent discrete time model of the plant, a kind of time-varying state feedback control algorithm is put forward a strategy to obtain real-time update. The stability of the closed-loop switch sufficient conditions is derived using the theorem of linear system. It shows that the method studies of industrial network by two cases, i.e., a DC motor driven transport roll paper and a milling machine. Simulation study describes the effectiveness of the method to control these challenging problems.

Networked control of industrial automation systems—a new predictive method

A new method for real-time prediction of uncertain network transmission time delays and a method for closed-loop control of manufacturing and industrial plants through networks are introduced. The proposed delay prediction method is based on the multilayer perceptron neural model. In order to minimize the number of neurons in the first layer of the network and hence reducing the computational burden in a real-time implementation, a method for determination of the Markov order of the time delay sequence is presented. Using the predicted delay, and a zero-order hold equivalent discrete-time model of the plant, a time varying state feedback control algorithm with a real-time gain updating strategy is proposed. A sufficient condition for closed-loop stability is also derived using the switching theorem for linear systems. The proposed method is shown, through two industrial networked case studies, namely, a DC motor driving a transportation roller for paper sheets and a milling machine. Simulation studies depict the efficacy of the proposed method in controlling such challenging problems.

A neural network model approach to athlete selection

In order to determine the usefulness of neural models in optimisation of recruitment processes, statistical analyses were carried out on measured results of javelin throwers using a full take off. A group of 140 Polish junior javelin throwers took part in the research. In order to choose the optimum combination of model parameters the Hellwig method was used. Linear and multilayer perceptron neural models were constructed and used to calculate combinations of variables. Statistical analysis of the results showed that the linear model was not able to describe precisely the relationship between the dependent variable and independent variables for the investigated group of young javelin throwers. For the investigated group, the perceptron network with a 4-3-2-1 structure gave the best predictive relationship for sports results of the javelin throwers.

Neural modeling of laser surface treatments

This paper introduces a new multilayer perceptron neural model for laser surface heat treatments based on Green's function. Due to its high versatility, efficiency, and low simulation time, this model is suitable not only for the analysis and design of control systems but also for the development of an expert real time supervision system, allowing the detection and prevention of any failure during the treatment.

Modelling air pollution time-series by using wavelet functions and genetic algorithms

This paper proposes a novel approach based on the use of wavelet functions to model air pollution time series. One peculiarity of the approach is that of combining the use of wavelets and genetic algorithms to search for the best wavelet parameters. A case study, referring to the modelling of daily averages of SO2 time series recorded in the industrial area of Syracuse (Italy) is reported in order to compare the performance of a wavelet-based prediction model and a Multi-layer perceptron neural model. The results obtained show that there are no significant differences between the neural and the wavelet approach in terms of model performance and computational effort. There is however an appreciable advantage in using the proposed wavelet-based technique in terms of model readability.

A multilayer perceptron neural model for the differentiation of laplacian 3-D finite-element solutions

An MLP neural model is presented in order to evaluate accurately derivatives of rough finite-element numerical solutions to three-dimensional Laplacian electromagnetic problems. The adopted neural approach overcomes the limitations inherent to advanced postprocessing techniques based on Poisson integrals because it is applicable to domains of arbitrary shape. The training of the neural network is performed off-line by employing a modular class of harmonic polynomial functions. Accuracy can be predetermined at the user's convenience by suitably selecting the order of the polynomial functions in the off-line training. The tests performed show that accurate results are achieved with a negligible online computational effort. A further advantage of this neural model is its easy implementation in existing postprocessing modules.