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Predicting the future course of a sequential collection of an observable has several applications in diverse fields. Traditional techniques assume fixed linear models. In contrast, models based on artificial neural networks are adaptive and nonlinear; however these are typically trained offline. This paper focuses on time-series prediction using a neural network with a single hidden layer that is...
Online sequence learning from streaming data is one of the most challenging topics in machine learning. Neural network models represent promising candidates for sequence learning due to their ability to learn and recognize complex temporal patterns. In this paper, we present a comparative study of Hierarchical Temporal Memory (HTM), a neurally-inspired model, and other feedforward and recurrent artificial...
In order to use the method based on model to study the accurate control of cement combined grinding system, and improve its automatic control level, this paper proposes a extreme learning machine (ELM) online modeling method for combined cement grinding system. First of all, this paper analyzes the process of combined grinding system, based on the analysis of the process, we know that the speed of...
To help telecommunications operators accurately predict the terminal replacement behavior, and improve the success rate of marketing and the accuracy of resources devoting, huge user consumption data are used to build Deep Belief Network. The deep features that characterize the terminal replacement behavior are learned, through which a terminal replacement prediction model is conducted. Experiments...
There is the coupling relationship between financial innovation process system and environment system, which is embodied by the complex interactive stress and constraint mechanism between them. In this paper, financial innovation system as an example, is analyzed by Grey Model (GM(1, 1)) and Back Propagation Neural Network (BPNN) model, and then the coupling coordination situation between the financial...
Streaming sensorial data poses major computational challenges, such as, lack of storage, inapplicability of offline algorithms, and the necessity to capture nonstationary data distributions with concept drifts. Our goal is to build a learner framework that uses the current data and the knowledge from historical data to predict the next data in an efficient, unsupervised and online manner. Labeled...
Functional Magnetic Resonance Imaging (fMRI) has become an important diagnostic tool for measuring brain haemodynamics. Previous research on analysing fMRI data mainly focuses on detecting low-level neuron activation from the ensued haemodynamic activities. An important recent advance is to show that the high-level cognitive status is recognisable from a period of fMRI records. Nevertheless, it would...
Predicting revenue from tenants for an enterprise having several malls cannot be easily done using conventional approach, such as spreadsheet or manual calculations. Such an enterprise has abundant data yet inadequate resources to analyze such data. This paper presents the data mining method, namely the Artificial Neural Network (ANN), to predict the revenue based on the previous data. ANN can help...
In this paper we propose to use decision boundary to analyze classifiers. Two algorithms called decision boundary point set (DBPS) and decision boundary neuron set (DBNS) are proposed to obtain the data on the decision boundary. Based on DBNS, a visualization algorithm called SOM based decision boundary visualization (SOMDBV) is proposed to visualize the high-dimensional classifiers. The decision...
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