Neural Networks with Java: glossary


a
activation area	refers to: Kohonen Feature Map A value that indicates the area of influence of the most activated neuron (the center of activation) on other map neurons. The activation is spread out around this center (maximum activation) and decreases the greater the distance to this center is.
activation function	refers to: neuron A mathematical function that a neuron uses to produce an output refering to its input value. Usually this input value has to exceed a specified threshold value that determines, if an output to other neurons should be generated. see also: hard limiter, signum activation, sigmoid activation
API	refers to: general term, abbreviation Application Programming Interface. All classes and routines that a programming language implements. The API of Java is called JDK.
artificial intelligence (AI)	refers to: general term A research discipline whose aim is to make computers able to simulate human abilities, especially the ability to learn. AI is separated in e.g. neural net theory, expert systems, robotics, fuzzy control systems, game theory etc.
AWT	refers to: general term, abbreviation Abstract Window Toolkit. Classes and routines, used to create a user interface. Java provides classes that are system-independent in its package `java.awt`.
axon	refers to: neural cell The part of a biological neural cell that contains the dendrites, connecting this neural cell to other cells. The incoming stimulation of a neural cell is transported from the cell's core through the axon to the outgoing connections.

b
backpropagation	refers to: learning algorithm A learning algorithm used by neural nets with supervised learning. Special form of the delta learning rule. see also: forwardpropagation
Backpropagation Net	refers to: neural net A feedforward type neural net. Has one input layer, one output layer and at least one hidden layer. Mainly used for pattern association. see also: backpropagation
bias	refers to: neural net A "pseudo" input of a neural net with any value except zero. Its purpose is to generate different inputs for different input patterns given to the net.

d
delta learning rule	refers to: learning algorithm A learning algorithm used by neural nets with supervised learning. Effects the changing of weights by multiplying a neuron's input with the difference of its output and the desired output and the net's learning rate.
dendrite	refers to: neural cell The connections between biological neural cells. Electrical stimulation is transported from cell to cell using these connections.

e
error	refers to: neural net, output A value that indicates the "quality" of a neural net's learning process. Used by neural nets with supervised learning, by comparing the current output values with the desired output values of the net. The smaller the net's error is, the better the net had been trained. Usually the error is always a value greater than zero.

f
feedback type	refers to: neural net A specific connection structure of a neural net, where neurons of one neuron layer may have connections to neurons of other layers and also to neurons of the same layer. An example of such a net type is the Hopfield Net. see also: feedforward type
feedforward type	refers to: neural net A specific connection structure of a neural net, where neurons of one neuron layer may only have connections to neurons of other layers. An example of such a net type is the Perceptron. see also: feedback type
forwardpropagation	refers to: learning algorithm The output values of a neural net's neurons are only propagated through the net in one direction, from the input layer to the output layer. see also: backpropagation

g
GUI	refers to: general term, abbreviation Graphical User Interface. The graphical environment of a software application.

h
hard limiter	refers to: neuron, activation function A specific type of a neuron's activation function. see also: activation function
Hebb learning rule	refers to: learning algorithm A learning algorithm used by neural nets with supervised learning, especially the Perceptron. Effects the changing of weights by multiplying a neuron's input with its output and the net's learning rate.
hidden layer	refers to: neural net A type of neuron layer that lies between a neural net's input and output layers. Called "hidden", because its neuron values are not visible outside the net. The usage of hidden layers extends a neural net's abilities to learn logical operations. see also: Multi-Layer-Perceptron
Hopfield Net	refers to: neural net A feedback type neural net. Has only one "layer", build of a number of neurons, where each neuron is connected to all other neurons. Mainly used for pattern association. see also: simulated annealing

i
input	refers to: neuron, input layer A set of values, called "pattern", that is passed to a neural net's input neuron layer. The elements of those patterns are usually binary values. see also: input layer, output
input layer	refers to: neuron layer, neural net The first layer of a neural net, that accepts certain input patterns and generates output values to the successing weight matrix. see also: input, weight matrix
IP	refers to: general term, abbreviation Internet Protocol. A transmission protocol used to transport data across the Internet.

j
JDK	refers to: general term, abbreviation Java Developers Kit. An extensive set of Java classes, suitable for different purposes. The classes are enclosed in "packages", each covering a certain topic (networking, user interface,...). see also: API

k
Kohonen Feature Map	refers to: neural net A feedforward / feedback type neural net. Built of an input layer thats neurons are connected with each neuron of another layer, called "feature map". The feature map can be one- or two-dimensional and each of its neurons is connected to all other neurons on the map. Mainly used for classification. see also: selforganization

l
learning algorithm	refers to: neural net A mathematical algorithm that a neural net uses to learn specific problems. see also: backpropagation, delta learning rule, forwardpropagation, Hebb learning rule, simulated annealing
learning rate	refers to: learning algorithm A changeable value used by several learning algorithms, which effects the changing of weight values. The greater the learning rate, the more the weight values are changed. Is usually decreased during the learning process.

m
Multi-Layer-Perceptron	refers to: neural net A feedforward type neural net. Built of an input layer, at least one hidden layer and one output layer. Mainly used for pattern association. see also: backpropagation, hidden layer, Perceptron

n
neuron	refers to: neuron layer, neural net An element of a neural net's neuron layer. see also: activation function, threshold
neuron layer	refers to: neural net A layer of a neural net. The different layers of a neural net are connected by weight matrices. see also: input layer, hidden layer, output layer, weight matrix

o
object orientation (OO)	refers to: general term A method of software-engineering. The main goal of object orientation is to develope reusable software components.
output	refers to: neuron, output layer A value or a set of values (pattern), generated by the neurons of a neural net's output layer. Used to calculate the current error value of the net. see also: output layer, input, error, supervised learning
output error	see error
output layer	refers to: neuron layer, neural net The last layer of a neural net, that produces the output value of the net. see also: output

p
Perceptron	refers to: neural net A feedforward type neural net. Built of one input layer and one output layer. Mainly used for pattern association. see also: Multi-Layer-Perceptron
propagation	refers to: neural net, learning algorithm The passing of values and errors through the different layers of a neural net during its learning process. see also: forwardpropagation, backpropagation
propagation function	refers to: neural net, learning algorithm A function that is used to transport values through the neurons of a neural net's layers. Usually, the input values are added up and passed to an activation function, which generates an output. see also: forwardpropagation, backpropagation, activation function

s
selforganization	refers to: Kohonen Feature Map, learning algorithm A learning algorithm used by the Kohonen Feature Map neural net. During its learning process, the neurons on the net's feature map are organizing themselves depending on given input values. This will result in a clustered neuron structure, where neurons with similar properties (values) are arranged in related areas on the map. see also: Kohonen Feature Map, learning algorithm
sigmoid activation	refers to: neuron, activation function A specific type of a neuron's activation function. see also: activation function
signum activation	refers to: neuron, activation function A specific type of a neuron's activation function. see also: activation function
simulated annealing	refers to: learning algorithm A specific type of a learning algorithm, especially for feedback type neural nets. see also: Hopfield Net
supervised learning	refers to: neural net, learning algorithm A specific type of a learning algorithm. The output (pattern) of the net is compared with a target output (pattern). Depending on the difference between these patterns, the net error is computed. see also: unsupervised learning, error

t
TCP	refers to: general term, abbreviation Transmission Control Protocol. A protocol used to transport data across the Internet.
thermodynamical model	refers to: neural net Another expression for feedback type neural nets. Called "thermodynamical", because the term energy is used instead of error. see also: Hopfield Net
threshold	refers to: neuron, activation function A specific value that must be exceeded by a neuron's activation function, before this neuron generates an output. see also: neuron, activation function

u
UDP	refers to: general term, abbreviation User Datagram Protocol. A protocol used to transport data across the Internet in form of datagrams (small packets that are sent independently).
unsupervised learning	refers to: neural net, learning algorithm A specific type of a learning algorithm, especially for selforganizing neural nets like the Kohonen Feature Map. Unlike supervised learning, no target patterns exist. see also: supervised learning, selforganization
URL	refers to: general term, abbreviation Uniform Resource Locator. An address of a certain location on the World Wide Web. An URL usually has the following form: `http://www.[servername].[country\|category]`.

w
weight	refers to: neural net, weight matrix An element of a weight matrix. A connection between two neurons with a value that is dynamically changed during a neural net's learning process. see also: weight matrix, learning algorithm
weight matrix	refers to: neural net The connection structure between two neuron layers of a neural net. Its elements, the weights, are changed during the net's learning process. Each neural net has at least one weight matrix. see also: neuron layer, weight

x
xor problem	refers to: general term A logical operation with the following values: x y XOR false false false false true true true false true true true false Due to its contradiction, this function can only be learned by neural nets with at least one hidden layer. see also: Multi-Layer-Perceptron, hidden layer

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