In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

Neural networks and fuzzy inference systems are becoming well recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. Also, by combining these two features, more versatile and robust models, called 'neuro-fuzzy' architectures have been developed. The motivation behind the use of neuro-fuzzy approaches is based on the complexity of real life systems, ambiguities on sensory information or time-varying nature of the system under investigation. In this way, the present contribution concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation and to reduce the chattering phenomenon introduced by sliding mode control, which is very harmful to the actuators in our case and may excite the unmodeled dynamics of the system. The type of the neuro-fuzzy system used here is called:' adaptive neuro fuzzy inference controller (ANFIS)'. This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Simulation results reveal some very interesting features.

No abstract

Problem statement: Neural networks and fuzzy inference systems are becoming wellrecognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. Also, by combining these two features, more versatile and robust models, called "neuro-fuzzy" architectures have been developed. The mo Approach: Motivation behind the use of neuro-fuzzy approaches was based on the complexity of real life systems, ambiguities on sensory information or time-varying nature of the system under investigation. In this way, the present contribution concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation, ensure more robustness of the overall system and to reduce the chattering phenomenon introduced by sliding mode control which is very harmful to the actuators in our case and may excite the unmodeled dynamics of the system. Results: In fact, the aim of such a research consists first in simplifying the control of the motor by decoupling between two principles variables which provoque the torque in the motor by using the feedback linearization method. Then, using sliding mode controllers to give our process more robustness towards the variation of different parameters of the motor. However, the latter technique of control called sliding mode control caused an indesirable phenomenon which harmful and could leads to the deterioration of the inverter's components called "chattering". So, here the authors propose to use neuro-fuzzy systems to reduce this phenomenon and perform the performances of the adopted control process. The type of the neuro-fuzzy system used here is called": Adaptive Neuro Fuzzy Inference System (ANFIS)". This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Conclusion: Therefore, from a control design consideration, the adopted neuro-fuzzy system has opened up a new direction that allows for the design of robust controllers for uncertain non-linear dynamical systems without resorting to system model simplifications and linearization and without imposing structural conditions on system uncertainties. On the other hand, it is important to say that this approach permits to improve the performance of the controlled system only by choosing the appropriate form of the membership functions (shape, triangular…) and a good partionnement of the universe of discourse of the diverse variables. Finally the obtained simulation results prove that the objectives of the authors where attempt by a significant reduction of the chattering and a good robustness of the process towards parameter variation and external perturbation (load torque).

No abstract