Uploaded byProf. Neeta Awasthy
Artificial Neural Networks for NIU
The document outlines a course on artificial neural networks (ANNs) aimed at applying and evaluating neural network structures for various scientific and engineering problems. It discusses the principles of how the human brain learns, different types of networks, and learning methods including supervised, unsupervised, and reinforcement learning. Additionally, it covers applications of ANNs in fields such as pattern recognition, forecasting, and optimization, emphasizing the significance of unsupervised learning and the complexity of neural network training.
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Artificial Neural Networks for NIU
- 1. Artificial Neural Networks 14.3.2016 Prof. NeetaAwasthy Director, School of Engineering and Technology NOIDA INTERNATIONAL UNVERSITY, Greater Noida
- 2. Course Objective Tounderstand, successfully apply and evaluate Neural Network structures and paradigms for problems in Science, Engineering and Business.
- 3. PreRequisites It isexpected that, the audience has a flair to understand algorithms and basic knowledge of Mathematics, Logic gates and Programming
- 4. Outline Introduction Howthe human brain learns Neuron Models Different types of Neural Networks Network Layers and Structure Training a Neural Network Application of ANN
- 5. Introduction: Artificial Intelligence Artificial Intelligence techniques such as Neural networks, genetic algorithms and fuzzy logic are among the most powerful tools available for detecting and describing subtle relationships in massive amounts of seemingly unrelated data. Neural networks can learn and are actually taught instead of being programmed. Teaching mode can be supervised or unsupervised Neural Networks learn in the presence of noise
- 6. How the HumanBrain learns In the human brain, a typical neuron collects signals from others through a host of fine structures called dendrites. The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons.
- 7. A Neuron Model When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes. We conduct these neural networks by first trying to deduce the essential features of neurons and their interconnections. We then typically program a computer to simulate these features.
- 8. A Simple Neuron An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode.
- 9. The McCulloch-Pitts model Neuronswork by processing information. They receive and provide information in form of spikes. Inputs Output w2 w1 w3 wn . . . x1 x2 x3 … xn-1 xn y
- 10. Properties for McCulloch and Pitts Model Input is 0 or 1 Weights are -1, 0 or +1 Threshold is an integer Output is 0 or 1 Output is 1 if multiplication of weight and input is more than the threshold else Outputs 0 Represent the gates NOT, OR and AND with the help of this model Truth Table L=0 -1 x y x y 0 1 1 0 x=0
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- 14. OR gate v/sXOR gate
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- 16. Summary of thesimple networks Single layer nets have limited representation power (linear separability problem) Error drive seems a good way to train a net Multi-layer nets (or nets with non-linear hidden units) may overcome linear inseparability problem, learning methods for such nets are needed Threshold/step output functions hinders the effort to develop learning methods for multi- layered nets
- 17. Types of Problems Mathematical Modeling (Function Approximation) Classification Clustering Forecasting Vector Quantization Pattern Association Control Optimization
- 18. Training/ Learning Learningcan be of one of the following forms: Supervised Learning Unsupervised Learning Reinforced Learning The patterns given to classifier may be on: Parametric Estimation Non- Parametric Estimation
- 19. Machine Learning inANNs Supervised Learning − It involves a teacher that is scholar than the ANN itself. For example, the teacher feeds some example data about which the teacher already knows the answers.
- 20. Machine Learning inANNs Unsupervised Learning − It is required when there is no example data set with known answers. For example, searching for a hidden pattern. In this case, clustering i.e. dividing a set of elements into groups according to some unknown pattern is carried out based on the existing data sets present.
- 21. Machine Learning inANNs Reinforcement Learning − This strategy built on observation. The ANN makes a decision by observing its environment. If the observation is negative, the network adjusts its weights to be able to make a different required decision the next time.
- 22. Unsupervised Learning: why? Collecting and labeling a large set of sample patterns can be costly. Train with large amounts of unlabeled data, and only then use supervision to label the groupings found. In dynamic systems, the samples can change slowly. To find features that will then be useful for categorization. To provide a form of data dependent smart processing or smart feature extraction. To Perform exploratory data analysis, to find structure of data, to form proper classes for supervised analysis.
- 23. Measure of Dissimilarity: Define a metric or distance function d on the vector space λ as a real-valued function on the Cartesian product λX λ such that: Positive Definiteness: 0 < d(x,y) < ∞ for x,y ελ and d(x,y)=0 if and only if x=y Symmetry: d(x,y) = d(y,x) for x,y ελ Triangular Inequality: d(x,y) = d(x,z) + d(y,z) for x,y,z ελ Invariance or distance function: d(x+z,y+z) = d(x,y)
- 24. Error Computation MinkowskiMatrix or Lk norm Manhattan Distance or L1 norm Euclidian Distance or L2 norm Ln norm
- 25. No FreeLunch Theorem No classification method is inherently superior to any other.Classifier to be decide on the grounds of type of problem, prior distribution of samples, training data, cost function. Ugly Duckling Theorem There is no privileged or ‘best’ feature representation, and that even the notion of similarity between patterns depends implicitly on assumption that may or may not be correct.
- 26. Neural networks haveperformed successfully where other methods have not, predicting system behavior, recognizing and matching complicated, vague, or incomplete data patterns. Apply ANNs to pattern recognition, interpretation, prediction, diagnosis, planning, monitoring, debugging, repair, instruction, control Biomedical Signal Processing Biometric Identification Pattern Recognition System Reliability Business Target Tracking Neural Network Applications
- 27. Pattern Recognition System SensingSegmentation Classification (missing features & context) Post-processing (costs/ errors) Feature Extraction Input Output (decision)