neural network research papers-14

neural network research papers-14


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A neural network for PCA and beyond
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Principal Component Analysis (PCA) has been implemented by several neural methods. We discuss a Network which has previously been shown to find the Principal Component subspace though not the actual Principal Components themselves. By introducing a

Comparison of statistical and artificial neural network techniques for estimating past sea surface temperatures from planktonic foraminifer census data
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ABSTRACT. We present the first detailed and rigorous comparison of six different computational techniques used to reconstruct sea surface temperatures (SST) from planktonic foraminifer census data. These include the Imbrie–Kipp transfer functions (IKTF), the modern analog

Inlproving the Pricing of Options-A Neural Network Approach
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ABSTRACT In this paper we apply statistical inference techniques to build neural network models which are able to explain the prices of call options written on the German stock index DAX. By testing for the explanatory power of several input variables serving as network

Optimal learning in neural network memories
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Page 1. Optimal learning in neural network memories This article has been downloaded from IOPscience.  Optimal learning in neural network memories? LF Abbott and Thomas B Kepler Physics Department, Brandeis University, Waltham, MA 02254, USA

Neural-network-based parameter estimation in S-system models of biological networks
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ABSTRACT The genomic and post-genomic eras have been blessing us with overwhelming amounts of data that are of increasing quality. The challenge is that most of these data alone are mere snapshots of the functioning organism and do not reveal the organizational

Control chart pattern recognition using a new type of self-organizing neural network
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Control charts as used in statistical process control can exhibit six principal types of patterns: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Apart from normal patterns, all the other patterns indicate abnormalities in the process that must

Distance features for neural network-based recognition of handwritten characters
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Features play an important role in OCR systems. In this paper, we propose two new features which are based on distance information. In the first feature (called DT, Distance Transformation), each white pixel has a distance value to the nearest black pixel. The

Hybrid control for autonomous mobile robot navigation using neural network based behavior modules and environment classification
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A hybrid control architecture combining behavior based reactive navigation and model based environment classification has been developed. It is also hybrid in the sense that both competitive coordination and cooperative coordination are used for the BBC (Behavior

Tool wear monitoring of turning operations by neural network and expert system classification of a feature set generated from multiple sensors
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Feature extraction and decision-making is a matter of considerable interest for condition monitoring of complex phenomena with multiple sensors. In tool wear monitoring, the extraction of subtle aspects of signals from a range of transient and static events offers a

The integration of a priori knowledge into a Go playing neural network
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M Enzenberger ABSTRACT The best current computer Go programs are hand crafted expert systems. They are using conventional AI technics such as pattern matching, rule based systems and goal oriented selective search. Due to the increasing complexity of managing this kind of

Associative neural network
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An associative neural network (ASNN) is a combination of an ensemble of the feed-forward neural networks and the K-nearest neighbor technique. The introduced network uses correlation between ensemble responses as a measure of distance among the analyzed

Neural Network Versus Econometric Models in Forecasting Ination
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ABSTRACT Artificial neural network modelling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such models can usually find a solution for very

qUALITATIVE ANALYSIS OP A NEURAL NETWORK MODEL WITH MULTIPLE TIME DELAYS
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We consider a simplified neural network model for a ring of four neurons where each neuron receives two time delayed inputs: One from itself and another from the previous neuron. Local stability analysis of the positive equilibrium leads to a characteristic equation

On-line tool condition monitoring system with wavelet fuzzy neural network
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In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists

A high performance neural network for solving nonlinear programming problems with hybrid constraints
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ABSTRACT A continuous neural network is proposed in this Letter for solving optimization problems. It not only can solve nonlinear programming problems with the constraints of equality and inequality, but also has a higher performance. The main advantage of the

A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction dataNeural network for modelling spatial interaction data
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ABSTRACT. Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this

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