modified probabilistic neural network
Adaptive complex modified probabilistic neural network in digital channel equalization
... ADAPTIVE COMPLEX MODIFIED PROBABILISTIC NEURAL NETWORK IN DIGITAL CHANNEL EQUALIZATION.. James P.[r] ...
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A tunable approximately piecewise linear model derived from the modified probabilistic neural network
... The Modified Probabilistic Neural Network structure allows it to model data by weighting piecewise linear models associated with each of the network’s radial basis functions in[r] ...
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Center reduction algorithm for the modified probabilistic neural network equalizer
... (2001) Center reduction algorithm for the modified probabilistic neural network equalizer.. In: Proceedings of the International Joint.[r] ...
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An Efficient Method for Automatic Classification of Brain MRI using Feature Selection and Modified Probabilistic Neural Network
... classification. Modified probabilistic neural network (MPNN) classification was used in brain MRI images for training and testing for precision in tumor ...
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A modified probabilistic neural network (PNN) for nonlinear time series analysis
... The main purpose of this paper is to show how the Probabilistic Neural Network (PNN) architecture proposed by Specht can be easily adapted for nonlinear time series analysis[r] ...
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The modified probabilistic neural network as a nonlinear correlator detector
... A neural network can be developed to embody this model and provide a continuous nonlinear correlator output r(x) as the vector x is taken from the process signal by sliding [r] ...
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A modified probabilistic neural network signal processor for nonlinear signals
... In respect to peak output a n d detection delay enors all tlic neural networks perform fairly wcll and bcttcr tlic quadratic filtcr or the multiple corrclator w[r] ...
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Modified probabilistic neural network hardware implementation schemes
... equation (4). The first design is a virtual digital design and the second a fully parallel design. This RBF selects training centres which are within a specified c[r] ...
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Brain MRI Classification Using PNN and Segmentation Using K Means Clustering
... a modified Probabilistic Neural Network (PNN) model that is based on learning vector quantization (LVQ) with image and data analysis and manipulation techniques is proposed to carry out an ...
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Comparative study of effective wind power prediction methods with optimization algorithms for optimal economic dispatch of multiple fuel power plants
... basic Probabilistic Neural Network (PNN) is developed; WPNN is employed to forecast a one-hour ahead wind power for ensuring reliable power ...algorithm modified by employing the SQP method; ...
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Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model
... the network at a low cost without professional knowledge ...malicious network attacks. Therefore, network intrusion detection is getting more and more attention with the development of ...to ...
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Fingerprint Classification Using Kernel Smoothing Technique and Generalized Regression Neural Network and Probabilistic Neural Network
... A fingerprint classification method is introduced in this paper. Ridges’ angles, the common point, and the way by which ridges’ angles are distributed in images play a significant role in feature extraction phase. A ...
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Brain Tumor Classification using Probabilistic Neural Network
... paper, Probabilistic Neural Network with image and data processing techniques was employed to implement an automated brain tumor ...the Probabilistic Neural Network ...
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Support Vector Machine Neural Network Based Optimal Binary Classifier for Diabetic Retinopathy
... feed-forward neural networks trained with the standard back-propagation ...a network having a single layer of threshold units could classify a set of points perfectly if they were linearly ...two-layer ...
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Face Detection Using Probabilistic Neural Network (PNN)
... of neural network technology stemmed from the desire to develop an artificial system that could perform intelligent tasks similar to those performed by the human ...brain. Neural networks resemble ...
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Probabilistic Neural Network Assisted Cell Tracking and Classification
... layer each neuron computes a distance measure between the presented input vector and the training example represented by that pattern neuron. The PNN then use this distance measure to the Parzen window as weighting ...
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Digital Pen for Handwritten Digit and Gesture Recognition Using Trajectory Recognition Algorithm Based On Triaxial Accelerometer-A Review
... (micro inertial measurement unit (μIMU) with magnetometers), proposed by Luo et al. [10], was employed to compensate the orientation of the proposed digital writing instrument. If the orientation of the instrument was ...
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The JHU Machine Translation Systems for WMT 2016
... Preliminary results with this approach were in- conclusive. For example, on the Russian-English newstest2015, the BLEU score is 27.27 for 1-best vs. 27.31 for reranking. On German-English new- stest2015, the BLEU score ...
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Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... of neural networks become ap- parent only for large-scale problems, which are computationally intensive and not feasible for hand ...MATLAB, neural network al- gorithms can be quickly implemented, ...
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Analysis of Earth Embankment Structures using Performance-based Probabilistic Approach including the Development of Artificial Neural Network Tool.
... Although probabilistic and reliability analysis techniques are widely used within the geotechnical community, risk estimation tools that implements coupled numerical probabilistic approaches are lacking in ...
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