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Artificial neural network model & hidden layers in multilayer artificial neural networks
Artificial neural networks (ANNs) are computational models inspired by biological neural networks. ANNs can process large amounts of inputs to learn from data in a way similar to the human brain. There are different types of ANN architectures including single layer feedforward networks, multilayer feedforward networks, and recurrent networks. ANNs use supervised, unsupervised, or reinforced learning. The backpropagation algorithm is commonly used for training multilayer networks by propagating errors backwards from the output to adjust weights. Developing an ANN application involves collecting data, separating it into training and testing sets, designing the network architecture, initializing parameters/weights, transforming data, training the network using an algorithm like backpropagation, testing performance on new data, and
Artificial neural network model & hidden layers in multilayer artificial neural networks
- 1. Artificial Neural network model& hidden layers in multilayer artificial neural networks
- 2. Outline • Introduction • SingleLayer • Multi Layer • Learning • Backpropogation Algorithm
- 3. Introduction • Artificial NeuralNetwork are computational models inspired by biological neural network used for processing large number of inputs which are mostly unknown.
- 4. What is Problem? •Ourworld is full of data. After collection and organization, data, if we are lucky, becomes information. In today's interconnected world, information exists in electronic form that can be stored and transmitted instantly. Challenge is to understand, integrate, and apply information to generate useful knowledge.
- 5. What is Solution? •Interpretation requires data acquisition, cleaning (preparing the data for analysis), analysis, and presentation in a way that permits knowledgeable decision making and action. Key is to extract information about data from relationships buried within the data itself.
- 6. Why Human System? •Humanbrain is most powerful pattern recognition engine ever invented.
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- 10. History • 1943: McCulloch–Pitts“neuron” – Started the field • 1962: Rosenblatt’s perceptron – Learned its own weight values; convergence proof • 1969: Minsky & Papert book on perceptrons – Proved limitations of single-layer perceptron networks • 1982: Hopfield and convergence in symmetric networks – Introduced energy-function concept • 1986: Backpropagation of errors – Method for training multilayer networks • Present: Probabilistic interpretations, Bayesian and spiking networks
- 11. Single Layer • InputLayer • Output Layer
- 12. Multi Layer • InputLayer • Hidden Layer(s) • Output Layer
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- 14. • Instead ofprogramming computational system to do specific tasks, teach system how to perform task • To do this, generate Artificial Intelligence System- AI • AI systems must be adaptive – able to learn from data on a continuous basis
- 15. ANN Architecture a. SingleLayer Feed Forward
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- 18. ANN Architecture a. SingleLayer Feed Forward
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- 21. Clasification of learningstrategies
- 22. Supervised • Process ofusing desired output for training the NN • It employees a teacher to assist the network by telling the network what the desired response to a given input • Weights are modified according to the required output • Not practicable in all cases
- 23. Understanding Supervised and UnsupervisedLearning A BA B A B
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- 25. Supervised Learning • Itis based on a labeled training set. • The class of each piece of data in training set is known. • Class labels are pre-determined and provided in the training phase. A B A B A B ε Class λ Class λ Class λ Class ε Class ε Class
- 26. 26 A single perceptroncan be used to represent many boolean functions. • AND function : x1 x2 output 0 0 -1 0 1 -1 1 0 -1 1 1 1 <Training examples> Decision hyperplane : w0 + w1 x1 + w2 x2 = 0 -0.8 + 0.5 x1 + 0.5 x2 = 0 x1 x2 wixi output 0 0 -0.8 -1 0 1 -0.3 -1 1 0 -0.3 -1 1 1 0.2 1 <Test Results> - - - + x1 x2 -0.8 + 0.5 x1 + 0.5 x2 = 0 - - - + x1 x2 -0.8 + 0.5 x1 + 0.5 x2 = 0
- 27. 27 OR function • Thetwo-input perceptron can implement the OR function when we set the weights: w0 = -0.3, w1 = w2 = 0.5 x1 x2 o utput 0 0 - 1 0 1 1 1 0 1 1 1 1 <Training e x ample s > Decision hyperplane : w0 + w1 x1 + w2 x2 = 0 -0.3 + 0.5 x1 + 0.5 x2 = 0 x1 x2 Σwixi output 0 0 -0.3 - 1 0 1 0.2 - 1 1 0 0.2 - 1 1 1 0.7 1 <Te s t Re s ults > - + + + x1 x2 -0.3 + 0.5 x1 + 0.5 x2 = 0 - + + + x1 x2 -0.3 + 0.5 x1 + 0.5 x2 = 0
- 28. Unsupervised • No teacherRequired • Similar to the students learning on their own • Adaption rules • Adaption rule generate error signals
- 29. Reinforced • A teacheris assumed to b present but right answer is not given to the network • Network is given an indication whether output is right or wrong • Network use this indication to improve performance
- 30. Types of Neuronactivation function 1. Heaviside 1 if s>=T F(s)= 0 if s<T
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- 32. 3. Piecewise Linear 1if s>T F(s)= s if –T<s<T -1 if s<-T
- 33. Hardlimiter Unipolar Sigmoidal Bipolar Sigmoidal UnipolarMultimodal Radial Bessel’s Function
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- 36. 36 NEURAL NETWORK APPLICATION DEVELOPMENT Thedevelopment process for an ANN application has eight steps. • Step 1: (Data collection). • Step 2: (Training and testing data separation For a moderately sized data set, 80% of the data are randomly selected for training, 10% for testing, and 10% secondary testing. • Step 3: (Network architecture) Important considerations are the exact number of perceptrons and the number of layers.
- 37. 37 • Step 4:(Parameter tuning and weight initialization) • Step 5: (Data transformation) Transforms the application data into the type and format required by the ANN. • Step 6: (Training)
- 38. 38 • Step 7:(Testing) – The testing examines the performance of the network using the derived weights by measuring the ability of the network to classify the testing data correctly. – Black-box testing (comparing test results to historical results) is the primary approach for verifying that inputs produce the appropriate outputs. • Step 8: (Implementation) Now a stable set of weights are obtained.
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