neural
Neural Network Designer 0.2
Neural Network Designer becomes familiar to users as the first application which is based on the logical neural network concept. more>> <<less
Neural Network Designer is widely known as the first and effective application based on the logical neural network concept which providesJava Neural Network 1
intermediate to advanced Java programmer how to create neural networks more>> Programming Neural Networks in Java will show the intermediate to
advanced Java programmer how to create neural networks. This book
attempts to teach neural network programming through two mechanisms.
First the reader is shown how to create a reusable neural network
package that could be used in any Java program. Second, this reusable
neural network package is applied to several real world problems that
are commonly faced by IS programmers. This book covers such topics as
Kohonen neural networks, multi layer neural networks, training, back
propagation, and many other topics.
Chapter 1: Introduction to Neural Networks
Chapter 2: Understanding Neural Networks
Chapter 3: Using Multilayer Neural Networks
Chapter 4: How a Machine Learns
Chapter 5: Understanding Back Propagation
Chapter 6: Understanding the Kohonen Neural Network
Chapter 7: OCR with the Kohonen Neural Network
Chapter 8: Understanding Genetic Algorithms
Chapter 9: Understanding Simulated Annealing
Chapter 10: Eluding Local Minima
Appendix A. JOONE Reference
Appendix B. Mathematical Background
Appendix C. Compiling Examples under Windows
Appendix D. Compiling Examples under Linux/UNIX<<less
Sharky Neural Network 0.9b
Sharky Neural Network will make you satisfied because it is made for playing with neural networks classification. more>>
Sharky Neural Network 0.9b will make you satisfied because it is made for playing with neural networks classification.
Sharky Neural Network is a meural network classification results live view (like a movie). Sharky Neural Network is easy to use and ready to play with you. It has different shapes of training data sets so you can learn even with backpropagation algorithm.
Major Features:
- Easy, ready to play with.
- Live view.
- Many network architectures.
- Different shapes of training data sets.
- Learning with backpropagation algorithm.
- Optional momentum.
- Windows 2000/XP/Vista.
Java Neural Network Examples 1
Learn about neural network in Java. more>>
Java Neural Network Examples 1 is an open collection of neural network examples in Java. It includes JOONE examples, the traveling salesman, optical character recognition, handwriting recognition, fuzzy logic, and neural network pruning. Full source code is included.
Sharky Neural Network 0.9.Beta
Sharky Neural Network Neural network classification results live view Free software for playing with neural networks classification more>>
Sharky Neural Network Neural network classification results live view Free software for playing with neural networks classification. With neural network classification results live view (like a movie). Major features: * Easy, ready to play with. * Live view. * Many network architectures. * Different shapes of training data sets. * Learning with backpropagation algorithm. * Optional momentum. Applications: Education - For better understanding of neural networks.<<less
Neural Networks - DCT for Face Identification 1.0
Neural Networks - DCT for Face Identification more>>
Neural Networks - DCT for Face Identification
<<lessNeural Networks - DCT for Face Identification. Matlab source code. High information redundancyWeighscore Neural Network Command Line Tool 2
Weighscore Neural Network Command Line Tool is one of the most advantageous tools which may train neural networks using JDBC datasources and store the neural network in an XML file. more>> <<less
Java Neural Network Framework Neuroph 2.2
Develop and simulate common neural network architectures. more>>
Java Neural Network Framework Neuroph 2.2 is a flexible Java neural network framework designed to develop and simulate common neural network architectures.
It contains well designed Java library with small number of basic classes which correspond to basic NN concepts, and also has nice GUI neural network editor. It has been released as open source under the LGPL licence, and it's FREE for you to use it.
If you are beginner with neural networks, and you just want to try how they work without going into complicated theory and implementation, or you need them quickly for your research project the Neuroph is good choice for you. It is small, well documented, easy to use, and very flexible neural network framework.
Enhancements:
- Add momentum for backpropagation (learns more than two times faster),
- Add import training sets from txt files, error graph display,
- Add xml support, basic help system, sample image recognition application.
Speaker Recognition Based on Neural Networks 1.1
Speaker Recognition Based on Neural Networks lets you to identify people using the acoustic features of speech more>>
Image Compression with Neural Networks 1.0
Image Compression with Neural Networks is a very flexible and effective mathematic tool which have been applied to many problems, and have demonstrated their superiority over traditional methods when dealing with noisy or incomplete data. more>>
Image Compression with Neural Networks 1.0 is a very flexible and effective mathematic tool which have been applied to many problems, and have demonstrated their superiority over traditional methods when dealing with noisy or incomplete data. One such application is for image compression. Neural networks seem to be well suited to this particular function, as they have the ability to preprocess input patterns to produce simpler patterns with fewer components. This compressed information (stored in a hidden layer) preserves the full information obtained from the external environment.
Fast Artificial Neural Network Library 2.1.0 Beta
Fast Artificial Neural Network Library (fann) implements multilayer artificial neural networks in C more>>
Cross-platform execution in both fixed and floating point are supported. Fast Artificial Neural Network Library includes a framework for easy handling of training data sets.
Fast Artificial Neural Network Library is easy to use, versatile, well documented, and fast. Bindings to other programming languages and a GUI are also available.
A reference manual accompanies the library with examples and recommendations on how to use the library.
Neural Networks Based Signature Recognition 1.0
Signature verification technology utilizes the distinctive aspects of the signature to verify the identity of individuals more>>
Neural Networks Based Signature Recognition 1.0 is a helpful and intelligent system with the ability to utilize the distinctive aspects of the signature to verify the identity of individuals. The technology examines the behavioral components of the signature, such as stroke order, speed and pressure, as opposed to comparing visual images of signatures.
Unlike traditional signature comparison technologies, signature verification measures the physical activity of signing. While a system may also leverage a comparison of the visual appearance of a signature, or "static signature," the primary components of signature verification are behavioral. In the last few decades, many approaches have been developed in the pattern recognition area, which approached the off-line signature verification problem. There are two main approaches for off-line signature verification: static approaches and pseudodynamic approaches.
Personal Iris Recognition Using Neural Network 1.0
Artificial Neural Networks (ANNs) are programs designed to simulate the way a simple biological nervous system is believed to operate more>>
Personal Iris Recognition Using Neural Network 1.0 is developed as a useful and smart program which can simulate the way a simple biological nervous system is believed to operate. They are based on simulated nerve cells or neurons, which are joined together in a variety of ways to form networks. These networks have the capacity to learn, memorize and create relationships amongst data. ANN is an information-processing paradigm, implemented in hardware or software that is modeled after the biological processes of the brain.
An ANN is made up of a collection of highly interconnected nodes, called neurons or processing elements. A node receives weighted inputs from other nodes, sums these inputs, and propagates this sum through a function to other nodes. This process is analogous to the actions of a biological neuron. An ANN learns by example.
Wavelet-Neural Networks Based Face Recognition 1
Use transform coefficients in recognizing facial features. more>> <<less
Text-Independent Speaker Recognition Based on Neural Networks 1.0
Text-Independent Speaker Recognition Based on Neural Networks more>>
Text-Independent Speaker Recognition Based on Neural Networks
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