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شبکه های عصبی ارائه کنندگان : بنیامین قاسمی ساره منطقی فهرست مطالب معرفی شبکه عصبی معایب و مزایا شبکه عصبی مصنوعی استفاده در مدیریت انواع شبکه های عصبی مثال ارائه شده نحوه ی کارکرد جمع بندی مطالب End معرفی شبکه های عصبی What is a Neural Network? Collection of “neurons” Computes some function Takes input Produces output Can learn معرفی شبکه های عصبی Human Brain Function Human brain can generalize from abstract Recognize patterns in the presence of noise Recall memories Make decisions for current problems based on prior experience Neural Network Neurons Receives n-inputs Multiplies each input by its weight Applies activation function to the sum of results Outputs result شبکه عصبی مصنوعی What is an Artificial Neural Network (ANN)? The computational ability of a digital computer combined with the desirable functions of the human brain. شبکه عصبی مصنوعی Biological Neurons Artificial Neurons http://research.yale.edu/ysm/images/78.2/articles-neural-neuron.jpg http://faculty.washington.edu/chudler/color/pic1an.gif شبکه عصبی مصنوعی ساختار شبکه عصبی مصنوعی Input Units Influence Map Layer 1 Influence Map Layer 2 Hidden Units Output Units شبکه عصبی مصنوعی تناظر بین شبکه عصبی و شبکه مصنوعی Biological Neural Network Soma Dendrite Axon Synapse Artificial Neural Network Neuron Input Output Weight Soma Synapse Dendrites Axon Soma Input Signals Axon Out put Signals Synapse Dendrites Middle Layer Synapse Input Layer Output Layer نحوه ی کارکرد How does a neural network learn? A neural network learns by determining the relation between the inputs and outputs. By calculating the relative importance of the inputs and outputs the system can determine such relationships. Through trial and error the system compares its results with the expert provided results in the data until it has reached an accuracy level defined by the user. With each trial the weight assigned to the inputs is changed until the desired results are reached. نحوه ی کارکرد How the Process Works ? Step 1: Initialisation Set initial weights to random numbers in your range Step 2: Activation Activate the perceptron by applying inputs and desired output.( training set data) Calculate the actual output at iteration . نحوه ی کارکرد Step 3: Weight training Update the weights of the perceptron.The weight correction is computed by the delta rule Step 4: Iteration Increase iteration p by one, go back to Step 2 and repeat the process until convergence. انواع شبکه های عصبی Types of Networks: Multi-Layer-Perceptron Hopfield Net Kohonen Feature Map Adaptive Resonance Theory (Art), Fussy ArtMap Different Learning Algorithms: Type of Learning: Supervised Unsupervised Backpropagation Delta Learning Rule Forward Propagation Hebb Learning Rule Simulated Annealing Genetic Algorithms انواع شبکه های عصبی Muli-Layer Perceptron Hopfield Net معایب و مزایا استفاده از شبکه ها Neural Networks can be extremely complex and hard to use The programs are filled with settings you must input and a small amount of data will cause your predictions to have error The results can be very hard to interpret as well Dead-end situations are hard to avoid Neural networks can find relations that no one ever guess they exist Since they are data dependent performance will improve as sample size increases Regression performs better when theory or experience indicates an underlying relationship استفاده در مدیریت Marketing Trading and financial forecast Future price estimation Exchange rate forcast Bankruptcy prediction Stock performance and selection Portfolio assignment and optimization مثال کاربردی Large amount of data available in databases Customers data available in firm’s own database or can be supplied by companies which sell these information These information can be applied for marketing purposes e.g. direct marketing مثال کاربردی Direct marketing drives high cost Targeting customers who are more likely to spend money ! Direct mailing to customers مثال کاربردی In this example one charity organization apply direct mailing promotion to raise their funds Neural network applied to target selection in this case Neural network should determine those customers in data base who would be interested in the offer being maid Neural network in a learning system which can adapt the nonlinearity in the data to capture complex مثال کاربردی There can be different types of databaises with variety of data There should be most important aspects of a successful mailing compa Analytical methods (data mining, sensitivity analysis, … ) Experiment ( some researches , literature , …) Causal relations experties مثال کاربردی Data mining offers following representations as purchase history: 1. 2. 3. Recency of purchase Frequency of purchase Monetary value These variables are called RFM variables مثال کاربردی In working with models like ANN enough care must be taken about the process & the data data preparation publish in–publish out Determining causal relations Knowledge about customer’s attitude & history مثال کاربردی Mailing strategy Who should be mailed and how frequent How frequently ** Should be organized How their promotional material should be organized مثال کاربردی Classification or prediction Asymmetrical misclassification costs Weigh misclassified responders Or target scoring to show customers willingness مثال کاربردی What is ANN job Trained to determine correct set of network parameters Good indication of the willingness according to network inputs Indeed, indication of responsive behavior regarding their characteristics A nonlinear regression model مثال کاربردی Network configuration feed-forward neural networks for practical purpases number of hidden layers methods: growing and pruning, heuristic search, optimization by evllutionary computation (e.g. GA). experiment,... مثال کاربردی selecting network parameters experiments show that one hidden layer provides model with sufficient accuracy in target selecting transfer function: hyperbolic tangent sigmoid or logistic sigmoid,... مثال کاربردی data preparetoin discription of raw data size of data set feature selection process selected features selecting suitabale training and validation sets مثال کاربردی description of raw data a well-known Dutch charuty organization more than 725000 supporters in the internal database database including: mailing dates, amount of donation, date of donation in response to a particular mailing,... مثال کاربردی size of data set aoge amount of records makes network too complex and slow data size should be large enough and not too big 1000 random records representativies of the whole data مثال کاربردی Feature selection neccessarily not all features are useful and meaningful for target selection features that sumarize the most important aspects RFM variables table 2: the features used for the charity case study جمع بندی مطالب Neural networks provide ability to provide more human-like AI Takes rough approximation and hardcoded reactions out of AI design (i.e. Rules and FSMs) Still require a lot of fine-tuning during development منابع Artificial Neural Network in Finance and Manufacturing,Joarder Kamruzzaman Neural Networks And Their Statistical Application, Clint Hagen Statistics Senior Seminar 2006 Neural Networks , Megan Vasta Artificial Intelligence & Neural Networks, Amir Hesami Interview with Jeff Hannan, creator of AI for Colin McRae Rally 2.0 Interview with Derek Smart, creator of AI for Battlecruiser: 3000AD Neural Netware, a tutorial on neural networks Sweetser, Penny. “Strategic Decision-Making with Neural Networks and Influence Maps”, AI Game Programming Wisdom 2, Section 7.7 (439 – 46) Russell, Stuart and Norvig, Peter. Artificial Intelligence: A Modern Approach, Section 20.5 (736 – 48) از توجه شما بسیار سپاسگزاریم ساره منطقی بنیامین قاسمی پاییز 1386