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Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition , they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge of cats, for example, that they have fur, tails, whiskers and cat-like faces.

Instead, they automatically generate identifying characteristics from the examples that they process. An ANN is based on a collection of connected units or nodes called artificial neurons , which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Artificial neural network

An artificial neuron that receives a signal then processes it and can signal neurons connected to it. In ANN implementations, the "signal" at a connection is a real number , and the output of each neuron is computed by some non-linear function of the sum of its inputs.

The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.

Neural Networks for Identification, Prediction and Control

Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer the input layer , to the last layer the output layer , possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would.

However, over time, attention moved to performing specific tasks, leading to deviations from biology. ANNs have been used on a variety of tasks, including computer vision , speech recognition , machine translation , social network filtering, playing board and video games , medical diagnosis and even in activities that have traditionally been considered as reserved to humans, like painting. Warren McCulloch and Walter Pitts [2] opened the subject by creating a computational model for neural networks. Hebb [4] created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning.

Farley and Wesley A. Clark [5] first used computational machines, then called "calculators", to simulate a Hebbian network. Rosenblatt [6] created the perceptron. In , Seppo Linnainmaa published the general method for automatic differentiation AD of discrete connected networks of nested differentiable functions. In , he applied Linnainmaa's AD method to neural networks in the way that became widely used. In , max-pooling was introduced to help with least shift invariance and tolerance to deformation to aid in 3D object recognition.

Geoffrey Hinton et al. In , Ng and Dean created a network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images.

Ciresan and colleagues [27] showed that despite the vanishing gradient problem, GPUs make backpropagation feasible for many-layered feedforward neural networks. ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with.

Neural network learning of optimal Kalman prediction and control. - PubMed - NCBI

They soon reoriented towards improving empirical results, mostly abandoning attempts to remain true to their biological precursors. Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others. The network forms a directed , weighted graph.

ANNs retained the biological concept of artificial neurons , which receive input, combine the input with their internal state activation and an optional threshold using an activation function , and produce output using an output function. The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image.

The important characteristic of the activation function is that it provides a smooth transition as input values change, i. The network consists of connections, each connection providing the output of one neuron as an input to another neuron. Each connection is assigned a weight that represents its relative importance. The propagation function computes the input to a neuron from the outputs of its predecessor neurons and their connections as a weighted sum.

The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the input layer. The layer that produces the ultimate result is the output layer. In between them are zero or more hidden layers. Single layer and unlayered networks are also used.

Between two layers, multiple connection patterns are possible. They can be fully connected , with every neuron in one layer connecting to every neuron in the next layer. They can be pooling , where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer.