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We present a deep learning architecture for learning fuzzy logic expressions. Our model uses an innovative, parameterized, differentiable activation function that can learn a number of logical operations by gradient descent. This activation function allows a neural network to determine the relationships between its input variables and provides insight into the logical significance of learned network...
Regression testing is an important activity performed to ensure thatchanges in the baseline version of the system do not influence thealready tested part of the system. It becomes difficult to run the entiretest suite due to constrained or limited resources. A subset of test casesthat is as efficient as the original test suite is searched as optimal suite.Computational intelligence approaches has...
Fuzzy predictive controllers have been applied to several applications with good control performance. However, this methodology often leads to nonconvex optimization problems, which are difficult to solve for fast processes, i.e. processes with small sampling times. This paper proposes a new methodology to apply a fuzzy predictive controller in real-time by using a neural network architecture, which...
This paper describes the optimization of an ensemble neural network with fuzzy integration of responses based on type-1 and type-2 fuzzy logic. Genetic algorithms are used as method of optimization in this case. The time series that is being considered for the ensemble is the US Dollar/MX Peso exchange rate. Simulation results show that the ensemble approach produces good prediction of the exchange...
Despite the popularity of PID (Proportional-Integral-Derivative) controllers, their tuning aspect continues to present challenges for researches and plant operators. Various control design methodologies have been proposed in the literature, such as auto-tuning, self-tuning, and pattern recognition. The main drawback of these methodologies in the industrial environment is the number of tuning parameters...
This paper reviews soft computing approaches for reliability modeling and analysis of repairable systems. Although soft computing techniques such as neural networks and fuzzy systems and even stochastic methods have been employed for solving many different engineering complex problems, when it comes to reliability area traditional approaches are still preferred by industry. Unfortunately with the...
The continuous growth of broadband communications, multimedia services and Internet is absolutely related to the deployment and operation of optical networks. Despite optical fibers’ enormous physical bandwidth the development of optical networks for today’s advanced, reliable and guaranteed-type services, require an efficient management of the bandwidth together with an orthological and careful use...
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