Statistical learning theory

About: Statistical learning theory is a research topic. Over the lifetime, 1618 publications have been published within this topic receiving 158033 citations.

Elevator Traffic Flow Prediction Based on Wavelet Neural Networks

Abstract: The number of parameters of wavelet neural networks(WNN) increases by exponential form with input dimension and the convergence speed decreases.An algorithm is presented through using structural risk minimization(SRM) based on statistical learning theory.The novel algorithm can ensure great probability for global optimization.WNN based on SRM is also used to solve the problem of traffic flow prediction of elevator system,more optimal results than typical BP network are obtained.

Comparison of different models for wind speed prediction

Abstract: The paper presents several unconventional models for wind speed prediction based on fuzzy logic and neural network techniques. First, two fuzzy models of a position and position-gradient type are built on the basis of different meteorological data such as solar radiation, relative humidity, ambient temperature, atmospheric pressure etc. In order to obtain the fuzzy models for wind speed prediction, Sugeno-Yasukawa identification algorithm was employed. Next, a neuro-fuzzy model for wind speed prediction was build, based on statistical learning theory. The model presents a fuzzy inference system of Takagi-Sugeno type that uses an extended relevance vector machine for learning its parameters and number of fuzzy rules. Finally, a neural network approach was applied to build two different models for wind speed prediction based on extreme learning machine techniques. Both neural models represent single layer feedforward neural networks, with different learning algorithms. The first one applies classic extreme learning machine and the second one uses incremental extreme learning machine philosophy. The obtained models are compared for their generalization performance and approximation capability, and although they all possess excellent approximation capabilities, the neural model based on incremental extreme learning machine has shown the best simulation results.

Statistical learning controller for the energy management in a fuel cell electric vehicle

Abstract: This paper considers a high efficiency energy management control strategy for a hybrid fuel cell vehicle. The proposed switching architecture consists of a bank of neural network based controllers designed using statistical learning theory. The use of different power sources and the presence of different constraints make the power management problem highly nonlinear. Probabilistic and statistical learning methods are used to design the weights of a neural network and the switching strategy is used to implement different controllers designed on the considered operative conditions. The proposed controller increases the efficiency of the whole system and reduces the fuel consumption during a given path. Numerical results are obtained using the model of a real hybrid car, ?smile? developed by FAAM, using a stack of fuel cells as the primary power source in addition to ultracapacitors and a lithium battery pack. The results are compared with those of a single neural network based controller and the performance is shown to be satisfactory in terms of fuel consumption and the efficiency of the whole system.

Multivariate Analysis from a Statistical Point of View

Abstract: Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, many of the common approaches were borrowed from other elds. We clarify what the goal of a multivariate algorithm should be for the search for a new particle and compare dieren t approaches. We also translate the Neyman-Pearson theory into the language of statistical learning theory. Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, most of the common approaches were borrowed from other elds. Each of these algorithms were developed for their own particular task, thus they look quite different at their core. It is not obvious that what these dieren t algorithms do internally is optimal for the the tasks which they perform within high energy physics. It is also quite dicult to compare these dieren t algorithms due to the dierences in the formalisms that were used to derive and/or document them. In Section 2 we introduce a formalism for a Learning Machine, which is general enough to encompass all of the techniques used within high energy physics. In Sections 3 & 4 we review the statistical statements relevant to new particle searches and translate them into the formalism of statistical learning theory. In the remainder of the note, we look at the main results of statistical learning theory and their relevance to some of the common algorithms used within high energy physics.