An Introduction to Artificial Neural Networks Wu Ping
An Introduction to Artificial Neural Networks Wu Ping
Neural networks have seen an explosion of interest over the last few years,and are being successfully applied across an extraordinary range of problem domains,in areas as diverse as finance,medicine, engineering, geology and physics. Indeed. anywhere that there are problems of prediction, classification or control,neural networks are being introduced.This sweeping success can be attributed to a few key factors:
Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. This sweeping success can be attributed to a few key factors:
Power:sophisticated modeling techniques capable of modeling extremely complex functions, nonlinear. Ease of use:learn by example,user gathers representative data,and then invokes training algorithms to automatically learn the structure of the data. Knowledge the user need to have: 1.how to select and prepare data 2.how to select an appropriate neural network 3.how to interpret the results
• Power: sophisticated modeling techniques capable of modeling extremely complex functions, nonlinear. • Ease of use: learn by example, user gathers representative data, and then invokes training algorithms to automatically learn the structure of the data. Knowledge the user need to have: 1. how to select and prepare data 2.how to select an appropriate neural network 3. how to interpret the results
·What are ANNs? In what areas are ANNs used? ·How to use ANNs?
• What are ANNs? • In what areas are ANNs used? • How to use ANNs?
What are ANNs?
What are ANNs?
Neural networks grew out of research in Artificial Intelligence,it would be necessary to build systems with a similar architecture to reproduce intelligence.ANNs are the analogy to the Brain
Neural networks grew out of research in Artificial Intelligence, it would be necessary to build systems with a similar architecture to reproduce intelligence. ANNs are the analogy to the Brain
Neuron,the most basic element of the human brain 4 Parts of a Typical Nerve Cell Dendrites:Accept inputs Soma:Process the inputs Axon:Turn the processed inputs into outputs Synapses:The electrochemical contact between neurons
Neuron, the most basic element of the human brain
The Artificial Neuron is the basic unit of neural networks,it simulates the four basic functions of natural neurons: Inputs-x(n);connection weight-w(n);transfer function;output. 1=Zw,×1 Summation Y=f[l)Transfer ×0 +N 0 ×2 2 Sum Transfer O味p味 Path Processing 目lem ent Inputs Weights n
The Artificial Neuron is the basic unit of neural networks, it simulates the four basic functions of natural neurons: Inputs-x(n); connection weight-w(n); transfer function; output
Neuron receives a number of inputs (either from original data,or from the output of other neurons in the neural network).Each input comes via a connection that has a strength (or weight);these weights correspond to synaptic efficacy in a biological neuron.Each neuron also has a single threshold value.The weighted sum of the inputs is formed,and the threshold subtracted,to compose the activation of the neuron.The activation signal is passed through an activation function (also known as a transfer function)to produce the output of the neuron
Neuron receives a number of inputs (either from original data, or from the output of other neurons in the neural network). Each input comes via a connection that has a strength (or weight); these weights correspond to synaptic efficacy in a biological neuron. Each neuron also has a single threshold value. The weighted sum of the inputs is formed, and the threshold subtracted, to compose the activation of the neuron. The activation signal is passed through an activation function (also known as a transfer function) to produce the output of the neuron
Layers This describes an individual neuron.The neurons are grouped into layers. input layer-receive input form the external environment; output layer-communicate the output of the system to the user or external environment; Inputs and outputs correspond to sensory and motor nerves such as those coming from the eyes and leading to the hands. hidden layers-a number of hidden between these two layers. The input,hidden and output neurons need to be connected together
Layers This describes an individual neuron. The neurons are grouped into layers: • input layer-receive input form the external environment; • output layer-communicate the output of the system to the user or external environment; Inputs and outputs correspond to sensory and motor nerves such as those coming from the eyes and leading to the hands. • hidden layers-a number of hidden between these two layers. The input, hidden and output neurons need to be connected together