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Description: This the free version of my Basis of AI Backprop designed to
accompany my not yet published textbook, _The Basis of AI_. This
program contains enough features for students in an ordinary AI or
Neural Networking course. It includes the quickprop and delta-bar-delta
algorithms for faster training and a fast integer version for machines
without floating point hardware.
This particular package includes C source for DOS and UNIX, examples, an
ASCII manual and a number of DOS binaries. The binaries are for systems
with floating point hardware (bp.exe), one for systems without floating
point hardware (bp486sx.exe) and an integer version binary
(ibp486sx.exe) for systems without floating point hardware.
Platform: |
Size: 2386161 |
Author: dsoiufsodiuf |
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Description: This the free version of my Basis of AI Backprop designed to
accompany my not yet published textbook, _The Basis of AI_. This
program contains enough features for students in an ordinary AI or
Neural Networking course. It includes the quickprop and delta-bar-delta
algorithms for faster training and a fast integer version for machines
without floating point hardware.
This particular package includes C source for DOS and UNIX, examples, an
ASCII manual and a number of DOS binaries. The binaries are for systems
with floating point hardware (bp.exe), one for systems without floating
point hardware (bp486sx.exe) and an integer version binary
(ibp486sx.exe) for systems without floating point hardware.
Platform: |
Size: 2385920 |
Author: dsoiufsodiuf |
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Description: 里面有神经网络的一些程序和超宽带的一些程序，希望能有用-Neural network there are some procedures and some procedures for ultra-wideband, hoping to be useful
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Size: 2048 |
Author: |
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Description: using delta rule neural network
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Size: 39936 |
Author: 司迪 |
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Description: Precise and fast control chart pattern (CCP) recognition is important for monitoring process environments
to achieve appropriate control and to produce high quality products. CCPs can exhibit six types of
pattern: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Except for
normal patterns, all other patterns indicate that the process being monitored is not functioning correctly
and requires adjustment. This paper describes a new type of neural network for speeding up the training
process and to compare three training algorithms in terms of speed, performance and parameter complexity
for CCP recognition. The networks are multilayered perceptrons trained with a resilient propagation,
backpropagation (BP) and extended delta-bar-delta algorithms. The recognition results of CCPs show the
BP algorithm is accurate and provides better and faster results.
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Size: 374784 |
Author: singh |
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Description: BP neural network implementation with generalized delta learning rule and momentum-BP neural network in Matlab
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Size: 1024 |
Author: lin |
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Description: 神经网络感知器学习规则：Delta学习规则。采用Delta学习规则，进行权值调整，阈值变换函数采用双极性连续函数。程序简单易懂，希望对大家有所帮助！-Neural network perceptron learning rule: Delta learning rule. Delta learning rule used to carry out the right to adjust the value of the threshold bipolar transfer function using a continuous function. Procedures are simple and easy to understand, want to help you!
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Size: 62464 |
Author: 李洋 |
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Description: 模拟BP神经网络，已知四个输入向量和期待输出，可以通过hebbian和delta学习算法得到权重值，从而构建网络-BP neural network simulation, the four known input vector and look forward to the output, you can get hebbian and delta weight learning algorithm to build the network
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Size: 1024 |
Author: 孙涛 |
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Description: some neural network rules Matlab code such as:perceptron, delta, widrow exist in this document
Platform: |
Size: 1024 |
Author: fateme |
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Description: 人工神经网络ANN程序，带图形界面，可以选用不同学习规则(Hebb, Delta, LMS, Conceptron, Correlation)和不同的转移函数-ANN artificial neural network program with a graphical interface, you can use different learning rules (Hebb, Delta, LMS, Conceptron, Correlation) and different transfer functions
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Size: 8192 |
Author: zoe |
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Description: 一个神经网络计算的库，实现几个通用神经网络体系和训练方法，自识别图，弹性网络等.like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning.-In this article, a C# library for neural network computations is described. The library implements several popular neural network architectures and their training algorithms, like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning. The usage of the library is demonstrated on several samples:
• Classification (one-layer neural network trained with perceptron learning algorithms)
• Approximation (multi-layer neural network trained with back propagation learning algorithm)
• Time Series Prediction (multi-layer neural network trained with back propagation learning algorithm)
• Color Clusterization (Kohonen Self-Organizing Map)
• Traveling Salesman Problem (Elastic Network).
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Size: 242688 |
Author: calford |
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Description: Neural Network LAM. A number of procedures are described for finding delta E/ delta W/sub ij/ where E is an error functional of the temporal trajectory of the states of a continuous recurrent network and w/sub ij/ are the weights of that network-Neural Network LAM. A number of procedures are described for finding delta E/ delta W/sub ij/ where E is an error functional of the temporal trajectory of the states of a continuous recurrent network and w/sub ij/ are the weights of that network
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Size: 1024 |
Author: betitogc2 |
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Description: it is a program to demonstrate the delta learning process in neural network.
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Size: 1024 |
Author: sam |
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Description: This the Delta rule for Artificial Neural Network for A-This is the Delta rule for Artificial Neural Network for ANN
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Size: 1024 |
Author: ADK |
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Description: rbf神经网络函数拟合。径向基函数，由样本选出固定中心值和delta-RBF neural network function fitting.Radial basis function (RBF), fixed center value chosen by the sample and the delta
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Size: 2048 |
Author: Eva |
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Description: To implement the NEURAL NETWORK ALGORITHMS using matlab.it contains
HEBB-DELTA LEARNING RULE
LINEAR SEPERABILITY OF INPUT DOMAIN
PATTERN RECOGNITION
HOPFIELD NETWORK
BACK PROPAGATION
Platform: |
Size: 3895 |
Author: karanmanohar98@gmail.com |
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