人工智能神经网络及其语言DevelopmentOfNeuralNetworkTheoryForArtificialLife-Thesis,MatlabAndJavaCode,Cavuto.pdf
《人工智能神经网络及其语言DevelopmentOfNeuralNetworkTheoryForArtificialLife-Thesis,MatlabAndJavaCode,Cavuto.pdf》由会员分享,可在线阅读,更多相关《人工智能神经网络及其语言DevelopmentOfNeuralNetworkTheoryForArtificialLife-Thesis,MatlabAndJavaCode,Cavuto.pdf(126页珍藏版)》请在文库网上搜索。
1、THE COOPER UNIONALBERT NERKEN SCHOOL OF ENGINEERINGA N E XPLORATION AND D EVELOPMENT OF C URRENTA RTIFICIAL N EURAL N ETWORK T HEORY AND A PPLICATIONSWITH E MPHASIS ON A RTIFICIAL L IFEbyDavid J. CavutoA thesis submitted in partial fulfillmentof the requirements for the degree ofMaster of Engineerin
2、gMay 6, 1997THE COOPER UNION FOR THE ADVANCEMENT OF SCIENCE AND ARTTHE COOPER UNION FOR THE ADVANCEMENT OF SCIENCE AND ARTALBERT NERKEN SCHOOL OF ENGINEERINGThis thesis was prepared under the direction of the Candidates ThesisAdvisor and has received approval. It was submitted to the Dean of theScho
3、ol of Engineering and the full Faculty, and was approved as partialfulfillment of the requirements for the degree of Master of Engineering._Dean, School of Engineering - Date_Prof. Simon Ben-Avi - DateCandidates Thesis AdvisoriAcknowledgmentsI would like to take this opportunity to thank, first and
4、foremost, my thesis advisor, Dr. SimonBen-Avi . His advice, both as a professor and as a friend, were and always will be invaluable.Moreover, I would like to thank the entire EE department faculty and staff for all the supportand encouragement (and toleration) they have shown me throughout the years
5、.I am deeply indebted to my friend and Big Brother Yashodhan Chandrahas Risbud (Yash!).Without the occasional smack in the head he needed to give me, I might not have made itthrough at all. Thanks for putting up with me.Kappa Phi Zeta Psi . My brothers supported me in the hard times and cheered me i
6、n thegood times. Can anyone ask for more?Special thanks to The Leib , Seamous , and of course, my Muffin .My utmost appreciation and thanks to my parents, George and Doris Cavuto . What can Isay? Thanks for everything. (Especially all that money!)And finally, a big old THANKS! to Peter Cooper for gi
7、ving me a place to work, learn, andgrow for the last six years. Anywhere else would have been just a school. The Cooper Unionhas been my home. DJCDisclaimer: this thesis is entirely a product of my imagination. Any resemblance to actual work is purely coincidental.ii1. AbstractThe purpose of this st
8、udy is to explore the possibilities offered by current Artificial Neural Net(ANN) structures and topologies and determine their strengths and weaknesses. The biologicalinspiration behind ANN structure is reviewed, and compared and contrasted with existingmodels. Traditional experiments are performed
9、 with these existing structures to verify theoryand investigate more possibilities . This study is conducted to the end of examining thepossibility of using ANNs to create “artificial life,” which is defined here as a structure oralgorithm which displays characteristics typically only attributed to
10、biological organisms,usually nonrepeating, nonrandom processes. Although some ANN topology is shown to behighly similar to that of biological systems, existing ANN algorithms are determined beinsufficient to generate the desired type of behavior. A new ANN structure, termed a“Temperon”, is designed,
11、 which encompasses more properties in common with biologicalneurons than did its predecessors. A virtual environment based on turtle graphics is used as atestbed for a neural net built with the new type of neuron. Experiments performed with theTemperon seem to confirm its ability to learn in an unas
12、sisted fashion.iiiTable of Contents1. ABSTRACT ii2. BACKGROUND 12.1 B IOLOGICAL N ATURE OF N EURAL C ELLS 12.1.1 P HYSICAL S TRUCTURE OF BIOLOGICAL NEURON 12.1.1.1 Body, Axon, Dendrites, Synapse 12.1.1.2 Neurotransmitter 32.1.1.3 Sodium/Potassium Pump 42.1.1.4 Ionized pulse 62.1.1.5 All-or-Nothing C
13、ausation 82.1.2 M ATHEMATICAL R EPRESENTATION OF N ERVE C ELL PROCESSES 102.1.2.1 Mathematical correlation to the physical interconnections 102.1.2.2 Linear combination of inputs 112.1.2.3 Thresholding resulting in binary or near-binary outputs 122.2 A RTIFICIAL N EURAL N ETS AND THEIR A PPLICATIONS
14、 142.2.1 G ENERAL T HEORY 142.2.1.1 Purpose 142.2.1.2 Structure 142.2.1.3 Weight Updating 152.2.2 P ERCEPTRONS - C LASSIFICATION 152.2.2.1 Single Layer 152.2.2.2 MLP - Feedforward 182.2.3 H OPFIELD N ET - P ATTERN R ECOGNITION 212.2.4 G ENERALIZATIONS 222.3 O UR FRIEND A PLYSIA 242.3.1 G ENERAL O BS
15、ERVATIONS 242.3.2 S UMMARY OF R ELEVANT E XPERIMENTS 252.3.2.1 Habituation 252.3.2.2 Sensitization 262.3.3 R ELEVANCE AND RELATION TO NEURAL NETS 26iv3. APPROACHES 273.1 G ENERAL M ETHODS AND T OOLS USED 273.1.1 MATLAB ANN TOOLBOX 273.1.2 J AVA 293.1.2.1 Neuron Package 293.1.2.2 TurtleMouse Environm
16、ent 303.2 P ERCEPTRON EXPLORATION 323.2.1 E XPLORATIONS 323.2.1.1 Test Set 1 - Network Size Limits 353.2.1.2 Test Set 2 - Disjoint Set A 353.2.1.3 Test Set 3 - Enclosed Region 363.2.1.4 Test Set 4 - Disjoint Set B 383.2.2 C ONCLUSIONS 413.2.2.1 Partitioning of n-space 413.2.2.2 Sensitivity of n th l
17、ayer to n-1 th layer underspecification 413.2.2.3 Tendency to find simplest solution results in sometimes non-useful heuristics 423.3 S PEAKER D IFFERENTIATION 433.3.1 G ENERAL I DEA 433.3.2 A PPROACHES 433.3.3 C ONCLUSIONS 453.4 T EMPERON 473.4.1 E VOLUTION OF M ODEL INSPIRED BY A PLYSIA 483.4.2 D
18、ESCRIPTION OF M ODEL 483.4.3 D ESCRIPTION OF T ESTBED 493.4.4 V ARIOUS TEST SETS O VERVIEW 513.4.4.1 Test Set 1: Learning rule adjustments 523.4.4.2 Test Set 2: Number of neurons 553.4.4.3 Test Set 3: Number/Types of senses 553.4.4.4 Test Set 4: Obstacle position 563.4.4.5 Test Set 5: SDIC 563.4.5 C
19、 ONCLUSIONS 563.4.5.1 Overall Behavior 563.4.5.2 Learning rule changes 573.4.5.3 Responses to test sets 593.4.5.4 General Conclusions 61v4. CONCLUSIONS 635. FUTURE CONSIDERATIONS 676. APPENDICES 706.1 A PPENDIX A: MATLAB CODE 716.1.1 P ERCEPTRON EXPLORATION 716.1.1.1 HINTONEM.M 716.1.1.2 PLOTEM.M 71
- 配套讲稿:
如PPT文件的首页显示word图标,表示该PPT已包含配套word讲稿。双击word图标可打开word文档。
- 特殊限制:
部分文档作品中含有的国旗、国徽等图片,仅作为作品整体效果示例展示,禁止商用。设计者仅对作品中独创性部分享有著作权。
- 关 键 词:
- 人工智能 神经网络 及其 语言 DevelopmentOfNeuralNetworkTheoryForArtificialLifeThesis MatlabAndJavaCode Cavuto
链接地址:https://www.wenkunet.com/p-9825.html