1. شبكه هاي عصبي مصنوعي جلسه دوم تاريخچه شبكه هاي عصبي مصنوعي
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2. تاريخچه دوره ابتدايي (1943-1956) دوره شروع (1956-1968)
دوره تاريكي يا رخوت ( )
دوره تولد تازه ( )
دوره شكوفايي (1986 تا كنون)
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3. William James William James (1890): Association
“Mental facts cannot properly be studied apart from the physical environment of which they take cognizance”
Principle of association: “When two brain processes are active together or in immediate succession, one of them, on reoccurring tends to propagate its excitement into the other”
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4. دوره ابتدايي: 1943 McCulloch & Pitts مدل ساده نرون
Warren McCulloch (a psychiatrist and neuroanatomist) and Walter Pitts (a mathematician)
مدل ساده نرون
قابليت ساخت دروازه‌هاي منطقي And و Or
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5. دوره ابتدايي: 1949 Hebb قانون تعليم هِب:
اطلاعات در سيناپس‌ها ذخيره مي‌شود
نحوه تعليم (تغيير سيناپس‌ها)
قدرت ارتباط سيناپسي (وزن weight)
وقتي گروهي از نرونهاي با سيناپس‌هاي ضعيف فعال مي‌شوند، قدرت سيناپس‌ها افزايش مي‌يابد.
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6. دوره ابتدايي: 1954
Farley and Clark, in 1954 on an early digital computer at MIT, modeled a network of randomly connected neurons employing a modified Hebb learning rule that was able to learn to discriminate between two input patterns
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7. دوره شروع: 1957
Rosenblatt
اولين مدل شبكه Perceptron: (perception neuron)
قانون تعليم پرسپترون
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8. Perceptron - history Proposed by Rosenblatt et al. (1958-1962).
A large class of neural models that incorporate learning.
“Mark I” perceptron is built with a retina of 20X20 receptors.
Learned to recognize letters.
Created excitement, and hype. (ENIAC was built in 1945)
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9. دوره شروع: 1960
Widrow
مدل ديگري از نرون Adaline: (Adaptive Linear Element)
قانون تعليم Widrow-Hoff‌‌
تعميم به شبكه‌اي از نرونها Madaline: (Many Adaline)
مشكل اصلي Madaline در روش تعليم آن بود
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10. Adaline - history
An adaptive pattern classication machine (called Adaline, for adaptive linear). . .
Proposed by Widrow and Hoff.
“During a training phase, crude geometric patterns are fed to the machine by setting the toggle switches in the 4X4 input switch array. Setting another toggle switch (the reference switch) tells the machine whether the desired output for the particular input pattern is +1 or -1 . The system learns something from each pattern and accordingly experiences a design change.. (Widrow and Hoff; 1960)
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11. Adaline – Widrow-Hoff Learning
The learning idea is as follows:
Define an error function that measure the performance of the performance in terms of the weights, input, output and desired output.
Take the derivative of this function with respect to the weights, and modify the weights accordingly such that the error is decreased.
Also known as the Least Mean Square (LMS) error algorithm, the Widrow-Hoff rule, the Delta rule.
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12. Widrow - Hoff
Within a half hour of the time that the algorithm was written on the blackboard, Hoff had it working in hardware. [There was] a large analog computer in the building, right across the hallway. There was nobody in the computer room. We just went in, picked up a plugboard and Hoff wired it together. ... This happened on a Friday afternoon in the autumn of 1959.
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13. دوره رخوت: 1969 Minsky & Papert
در كتاب خود به بررسي perceptron پرداخته و نشان دادند كه پرسپترون قادر به حل مسئله XOR نيست.
آنان اشتباهاً نتيجه‌گيري كردند كه چند نرون متوالي نيز قادر به حل XOR نيست.
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14. - + - + Linearly Separable - OR X2 (1,1) (1,0) (0,0) (0,1) X1
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15. - + - + Linearly Separable - And X2 (1,1) (1,0) (0,0) (0,1) X1
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16. - + - + Not Linearly Separable - XOR X2 (1,1) (1,0) (0,0) (0,1) X1
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17. Dark Age
Marvin Minsky and Seymour Papert, in 1969, published Perceptrons, in which they mathematically proved that single-layer perceptrons were only able to distinguish linearly separable classes of patterns
While true, they also (mistakenly) speculated that an extension to multiple layers would lose the “virtue” of the perceptron’s simplicity and be otherwise “sterile”
In the 1950s and 1960s, symbolic AI and sub-symbolic Connectionism competed for prestige and funding
AI investigated higher-order cognitive problems—logic, rational thought, problem solving
Connectionism investigated neural models like the perceptron and struggled to find a learning algorithm for multi-layer perceptrons
As a result of Minsky & Papert’s Perceptrons, research in neural networks was effectively abandoned in the 1970s and early 1980s
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18. Dark Age
Shun-ichi Amari, in 1967, and Christoph von der Malsburgh, in 1973, published ANN models of self-organizing maps, but the work was largely ignored
Paul Werbos, in his 1974 PhD thesis, first demonstrated a method for training multi-layer perceptrons, essentially identical to Backprop but the work was largely ignored
Stephen Grossberg and Gail Carpenter, in 1980, established a new principle of self-organization called Adaptive Resonance Theory (ART), largely ignored at the time
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19. دوره تولد مجدد: 1982 Hopfield
Hopfield Network and the Auto－associative Memory Models
Again, in 1985, Hopfield proposed another Hopfield & Tank Network to solve the Traveling Salesman Problem
After these researches, the ANN models were again treated as a promising research area .
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20. دوره تولد مجدد: 1986
David Rumelhart, James (Jay) McClelland, and the PDP Research Group (Psychologists at MIT)
Error Back-Propagation
“Parallel Distributed Processing” in which generalized delta rule are explained
In addition, they explain how BPN can solve the XOR problem.
Until 1990, BPN had become one of the most popular and highly utilized ANN model.
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21. دوره شكوفايي Adaptive Resonance Theory (Grossberg; 1980)
Hopfield model (Hopfield; 1982, 1984)
Self-organizing maps (Kohonen; 1982)
Reinforcement learning (Sutton and Barto; 1983)
Simulated Annealing (Kirkpatrick et al.; 1983)
Boltzmann machines (Ackley, Hinton, Terrence; 1985)
Backpropagation (Rumelhart, Hinton, Williams; 1986)
ART-networks (Carpenter, Grossberg; 1992)
Support Vector Machines (1990) ……
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22. Mature Age
Up to now, the ANN models has been widely studied and many models has been proposed. Conferences and Journals are created for ANN studies, such as ICNN(International Conference on NN, IJCNN( International Joint Conference on NN, held by IEEE & INNS). Besides, many tools and software，such as SNNS and MatLab, are been developed for making applying NN easier．
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