A neural networks based model have been used in predicting of the stock market. One of the methods, as an intelligent data mining, is artificial neural network (ANN). In this paper represents how to predict a NASDAQ's stock value using ANNs with a given input parameters of share market. We used real exchange rate value of NASDAQ Stock Market index. This paper makes use generalized feed forward networks. The network was trained using input data of stock market price in between 2012 and 2013. It shows a good performance for NASDAQ stock market prediction.
Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM.
In the process of dealing with various problems that are related to the control and management of complex systems, uncertainty is always the issue. Most of the decisions to be made by engineers and governors are subject to a lack of data that causes uncertainty. Some information is not always accessible and insufficient at the time of decision-making (DM). It is important to use the concept of a human being's expert knowledge, so developing models to estimate and imitate human decision-making systems has been becoming necessary. This chapter will critically assess the fuzzy reliability theory and systems to demonstrate how fuzzy logic represents human behavior and non-linearity. The authors created a fuzzy inference system to model two different complex systems. Fuzzy approaches to real problems in DM are effective alternatives to traditional approaches. Fuzzy integrations improve DM models in five principal features: (1) expert knowledge, (2) uncertainty handling, (3) human and government behavior modeling, (4) flexible modeling, and (5) simpler representations.
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