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yes, its right, silize, the material of computers are made dont are carbono that have some chemical propieties that other elements dont have... and brain works with chemical reactions... about Turing machine, i dont understand very well what are you saying...A PC computer is not a neural net, I often find educated programmers making this error. Your PC is an evolved Turing machine. A neural net is a neural network.
well, i dont want to model the human brain, and think that neurones exist on ALL animals... a neural network dont must think to be usefull...Neural networks still have a long way to go before they can model the human brain.
yes, its right, you dot need nnworks to have ai, but neural networks have the facility to generalize patterns... is very usefull for pattern recognition... a lot of OCR programs use neural networks... in another sites i read that on a experiment with neural network was able to learn grammar as a children of 6 years old... my example show how make the recognition of a logic gate, but can be used to some pattern recognition... i dont say that its the best method to make something, i only a coder that read something about nnworks, and make a version for PB...Suffice to say, you don't need a neural net to have A.I. like responses in your game/program. As long as a language allows you to manipulate GATES (AND,IF,OR,NOT,NOR,etc..) you should be able to create some great A.I. programs.
Code: Select all
; First we initialize the variables
MaxNeuralNetNumber=1
MaxInputNumber=2
InputNumbers=2
InputComb=Pow(MaxInputNumber,2)
Dim W(MaxNeuralNetNumber,MaxInputNumber) ; this is the weights
Dim Net(MaxNeuralNetNumber) ; this is the output for the neurals network
Dim Output(MaxNeuralNetNumber) ; if the Net is greater than 0 the output gets 1
Dim Inputs(MaxNeuralNetNumber,MaxInputNumber) ; this set the inputs for the neural networks
Dim InputImportance.s(MaxNeuralNetNumber,Inputcomb) ; input importance is for the supervised learning
; and say to the neural network that this input
; must gets an output
W(1,0)=1 ; we set a first values for neural networks
For Weights=1 To InputNumbers ; exist also W(1,0) but this line is only to make more easy
W(1,Weights)=Random(5)-3 ; calculations
Next Weights
Ta.f=0.8 ; Ta.f is the learning constant, and must be between 0 and 1
; you can think that when this value was higher would be
; better, because you can think that it can learn more faster
; but usually not, a high value can make that weights of the
; neural networks change very faster, and dont get the
; optimal value.
InputImportance(1,1)="000" ; We set the input importance to learn AND function, it have
InputImportance(1,2)="010" ; this form "IIO" where I are the inputs bits, two in this case
InputImportance(1,3)="100" ; and O the correct output, its like teach a dog
InputImportance(1,4)="111" ; to make something, we give two bits, you try that the dog
; make something and if it made correct we give a cookie
; but we give to the neural network 1( a binary cookie ),
; the correct output.
OpenConsole()
For Rep=1 To 80
Inputs(1,1)=Random(1) ; set random inputs for two bits
Inputs(1,2)=Random(1)
; *
Net(1)=W(1,0) ; and we get an output for the net
For In=1 To InputNumbers
Net(1)+(Inputs(1,In)*W(1,In))
Next In
; **
If Net(1)=>0 ; and if Net its greater than 0 we get an output
Output(1)=#TRUE
Else
Output(1)=#FALSE
EndIf
PrintN(Str(Inputs(1,1))+Str(Inputs(1,2))+" - "+Str(Output(1)))
; ***
InputComb=Pow(InputNumbers,2) ; this is the learning part, first we get all input posibilities
Input.s=""
For In=1 To InputNumbers ; we get a string with the inputs like in input importance
Input.s+Str(Inputs(1,In))
Next In
For In=1 To InputComb ; and we check with our predefined inputimportance to
If Input.s=Left(InputImportance(1,In),InputNumbers) ; get the correct defined ouput
InputImportance=Val(Right(InputImportance(1,In),1))
EndIf
Next In
Increment=InputImportance-Output(1) ; get the difference of the output of the net and the correct output
For In=1 To InputNumbers
W(1,In)=W(1,In)+Ta.f*Increment*Inputs(1,In) ; and get the new weights
Next In
W(1,0)=W(1,0)+Ta.f*Increment
Input()
Next Rep
CloseConsole()