Neuroph
Encyclopedia
Neuroph is an object-oriented neural network
framework written in Java
. It can be used to create and train neural networks in Java programs. Neuroph provides Java class library as well as GUI tool easyNeurons for creating and training neural networks.
It is the open source
project hosted at SourceForge
, and the latest version 2.4 has been released under the Apache License
. Previous versions were licensed under LGPL.
, neuron layer, neuron connections, weight, transfer function
, input function, learning rule etc.
Neuroph supports common neural network architectures such as Multilayer perceptron
with Backpropagation
, Kohonen and
Hopfield networks.
All these classes can be extended and customized to create custom neural networks and learning rules.
Has built in support for image recognition.
Neural network
The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes...
framework written in Java
Java (programming language)
Java is a programming language originally developed by James Gosling at Sun Microsystems and released in 1995 as a core component of Sun Microsystems' Java platform. The language derives much of its syntax from C and C++ but has a simpler object model and fewer low-level facilities...
. It can be used to create and train neural networks in Java programs. Neuroph provides Java class library as well as GUI tool easyNeurons for creating and training neural networks.
It is the open source
Open source
The term open source describes practices in production and development that promote access to the end product's source materials. Some consider open source a philosophy, others consider it a pragmatic methodology...
project hosted at SourceForge
SourceForge
SourceForge Enterprise Edition is a collaborative revision control and software development management system. It provides a front-end to a range of software development lifecycle services and integrates with a number of free software / open source software applications .While originally itself...
, and the latest version 2.4 has been released under the Apache License
Apache License
The Apache License is a copyfree free software license authored by the Apache Software Foundation . The Apache License requires preservation of the copyright notice and disclaimer....
. Previous versions were licensed under LGPL.
Features
Neuroph core classes correspond to basic neural network concepts like artificial neuronArtificial neuron
An artificial neuron is a mathematical function conceived as a crude model, or abstraction of biological neurons. Artificial neurons are the constitutive units in an artificial neural network...
, neuron layer, neuron connections, weight, transfer function
Transfer function
A transfer function is a mathematical representation, in terms of spatial or temporal frequency, of the relation between the input and output of a linear time-invariant system. With optical imaging devices, for example, it is the Fourier transform of the point spread function i.e...
, input function, learning rule etc.
Neuroph supports common neural network architectures such as Multilayer perceptron
Multilayer perceptron
A multilayer perceptron is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate output. An MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a...
with Backpropagation
Backpropagation
Backpropagation is a common method of teaching artificial neural networks how to perform a given task. Arthur E. Bryson and Yu-Chi Ho described it as a multi-stage dynamic system optimization method in 1969 . It wasn't until 1974 and later, when applied in the context of neural networks and...
, Kohonen and
Hopfield networks.
All these classes can be extended and customized to create custom neural networks and learning rules.
Has built in support for image recognition.
See also
- Neural networkNeural networkThe term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes...
- SOM or KohonenSelf-organizing mapA self-organizing map or self-organizing feature map is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional , discretized representation of the input space of the training samples, called a map...
- RetropropagationBackpropagationBackpropagation is a common method of teaching artificial neural networks how to perform a given task. Arthur E. Bryson and Yu-Chi Ho described it as a multi-stage dynamic system optimization method in 1969 . It wasn't until 1974 and later, when applied in the context of neural networks and...