Artificial neural networks (ANNs) (Application (data mining, visualization…
Artificial neural networks (ANNs)
Models may not consistently converge on a single solution, firstly because many local minima may exist, depending on the cost function and the model. Secondly, the optimization method used might not guarantee to converge when it begins far from any local minimum.
Models' "capacity" property roughly corresponds to their ability to model any given function. It is related to the amount of information that can be stored in the network and to the notion of complexity.
Other neural network computational machines were created by Rochester, Holland, Habit and Duda (1956).
Farley and Clark (1954) first used computational machines, then called "calculators", to simulate a Hebbian network.
In the late 1940s, D.O. Hebb created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning.
Warren McCulloch and Walter Pitts (1943) created a computational model for neural networks based on mathematics and algorithms called threshold logic.
game playing and decision making
natural resource management
The learning rule is a rule or an algorithm which modifies the parameters of the neural network, in order for a given input to the network to produce a favored output. This learning process typically amounts to modifying the weights and thresholds of the variables within the network.
Artificial neurons are elementary units in an artificial neural network.
An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network.
Connections and weights
connection weight can be set using the method NeuralNetwork.setSynapse.
Biological neural network
The vertebrate nervous
The entire information processing system, i.e. the vertebrate nervous system, consists of the central nervous system and the peripheral nervous system, which is only a first and simple subdivision.
In neuroscience, a biological neural network is a series of interconnected neurons whose activation defines a recognizable linear pathway.The interface through which neurons interact with their neighbors usually consists of several axon terminals connected via synapses to dendrites on other neurons.
An (artificial) neural network is a network of simple elements called neurons, which receive input, change their internal state (activation) according to that input, and produce output depending on the input and activation.
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
An ANN is based on a collection of connected units or nodes called artificial neurons (a simplified version of biological neurons in an animal brain).