Artificial neural networks
- 2. WHAT IS AN ARTIFICIAL NEURAL NETWORKS ? •An artificial neural network consists of a pool of simple processing units which communicate by sending signals to each other over a large number of weighted connections.
- 3. COMPUTERS vs NEURAL NETWORKS Standard Computers • One CPU • Fast processing unit • Reliable unit • Static Infrastructure Neural Networks • Highly parallel processing • Slow processing units • Unreliable unit • Dynamic Infrastructure
- 4. Why Artificial Neural Networks? • There are two basic reasons why we are interested in building artificial neural networks (ANNs): • Technical viewpoint: Some problems such as character recognition or the prediction of future states of a system require massively parallel and adaptive processing. • Biological viewpoint: ANNs can be used to replicate and simulate components of the human (or animal) brain, thereby giving us insight into natural information processing.
- 5. NEURAL NETWORKS • The “building blocks” of the neural networks is called neurons. • In technical terms, we also refer to them as units or nodes. • Basically, each neuron • Receives input from many other neurons. • Changes its internal state(activation) based on the current input. • Sends one output signal to many other neurons, possibly including its input neurons (recurrent network).
- 6. NEURAL NETWORKS • Information is transmitted as a series of electric impulses, so-called spikes. • The frequency and phase of these spikes encodes the information. • In biological systems, one neuron can be connected to as many as 10,000 other neurons. • Usually, a neuron receives its information from other neurons in a confined area, its so-called receptive field.
- 7. How do our Brains work? • Brain is a massively parallel information processing system. • Our brains are huge network of processing elements. A typical brain contains a network of 10 billion neurons. • A Processing Unit • Dendrites: Input • Cell Body: Processor • Synaptic: Link • Axon: Output
- 8. ARTIFICIAL NEURAL NETWORK An artificial neural network (ANN) is either a hardware implementation or a computer program which strives to simulate the information processing capabilities of its biological exemplar. ANNs are typically composed of a great number of interconnected artificial neurons. The artificial neurons are simplified models of their biological counterparts. ANN is a technique for solving problems by constructing software that works like our brains.
- 9. How do ANNs work? • An Artificial neuron is an imitation of a human neuron Artificial Neuron
- 10. How do ANNs work? Output x1x2xm ∑ y Processing Input ∑= X1+X2 + ….+Xm =y . . . . . . . . . . . .
- 11. How do ANNs work? • Not all inputs are equal Output x1x2xm ∑ y Processing Input ∑= X1w1+X2w2 + ….+Xmwm =y w1w2 wm weights . . . . . . . . . . . . . . . . .
- 12. How do ANNs work? • The signal is not passed down to the next neuron verbatim. Transfer Function (Activation Function) Output x1x2xm ∑ y Processing Input w1 w2wm weights . . . . . . . . . . . . f(vk) . . . . .
- 13. How do ANNs work? • The output is a function of the input, that is affected by the weights, and the transfer functions
- 14. APPLICATIONS ● Pattern Recognition ● Investment Analysis ● Control Systems and analysis ● Mobile Computing ● Marketing and financial analysis ● Forecasting - Sales, market, research, meteorology
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