Neural networks can be used to determine relationships and patterns between inputs and outputs. A simple single layer feed forward neural network which has a to ability to learn and differentiate data sets is known as a perceptron.

By iteratively “learning” the weights, it is possible for the perceptron to find a solution to linearly separable data (data that can be separated by a hyperplane). In this example, we will run a simple perceptron to determine the solution to a 2-input OR.

X1 or X2 can be defined as follows:

X1 | X2 | Out |
---|---|---|

0 | 0 | 0 |

1 | 0 | 1 |

0 | 1 | 1 |

1 | 1 | 1 |

If you want to verify this yourself, run the following code in Matlab. Your code can further be modified to fit your personal needs. We first initialize our variables of interest, including the input, desired output, bias, learning coefficient and weights.

input = [0 0; 0 1; 1 0; 1 1]; numIn = 4; desired_out = [0;1;1;1]; bias = -1; coeff = 0.7; rand('state',sum(100*clock)); weights = -1*2.*rand(3,1);

The input and desired_out are self explanatory, with the bias initialized to a constant. This value can be set to any non-zero number between -1 and 1. The coeff represents the learning rate, which specifies how large of an adjustment is made to the network weights after each iteration. If the coefficient approaches 1, the weight adjustments are modified more conservatively. Finally, the weights are randomly assigned.

A perceptron is defined by the equation:

Therefore, in our example, we have w1*x1+w2*x2+b = out

We will assume that weights(1,1) is for the bias and weights(2:3,1) are for X1 and X2, respectively.

One more variable we will set is the iterations, specifying how many times to train or go through and modify the weights.

iterations = 10;

Now the feed forward perceptron code.

for i = 1:iterations out = zeros(4,1); for j = 1:numIn y = bias*weights(1,1)+... input(j,1)*weights(2,1)+input(j,2)*weights(3,1); out(j) = 1/(1+exp(-y)); delta = desired_out(j)-out(j); weights(1,1) = weights(1,1)+coeff*bias*delta; weights(2,1) = weights(2,1)+coeff*input(j,1)*delta; weights(3,1) = weights(3,1)+coeff*input(j,2)*delta; end end

A little explanation of the code. First, the equation solving for ‘out’ is determined as mentioned above, and then run through a sigmoid function to ensure values are squashed within a [0 1] limit. Weights are then modified iteratively based on the delta rule.

When running the perceptron over 10 iterations, the outputs begin to converge, but are still not precisely as expected:

out = 0.3756 0.8596 0.9244 0.9952 weights = 0.6166 3.2359 2.7409

As the iterations approach 1000, the output converges towards the desired output.

out = 0.0043 0.9984 0.9987 1.0000 weights = 5.4423 12.1084 11.8823

As the OR logic condition is linearly separable, a solution will be reached after a finite number of loops. Convergence time can also change based on the initial weights, the learning rate, the transfer function (sigmoid, linear, etc) and the learning rule (in this case the delta rule is used, but other algorithms like the Levenberg-Marquardt also exist). If you are interested try to run the same code for other logical conditions like ‘AND’ or ‘NAND’ to see what you get.

While single layer perceptrons like this can solve simple linearly separable data, they are not suitable for non-separable data, such as the XOR. In order to learn such a data set, you will need to use a multi-layer perceptron.

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My partner and I stumbled over here different page and thought I should check things out. I like what I see so now i am following you. Look forward to exploring your web page repeatedly.

hi… this is my scilab code which is im using ann_PERCEPTRON function in scilab.

// Define at the SCILAB command window, the training inputs and targets

p = [57.80; 65.71; 47.91; 23.22; 43.85; 27.22; 23.36; 30.31; 58.78; 29.25; 42.29; 27.31; 56.12; 51.13; 57.36; 46.45; 25.76; 50.89; 30.69; 29.34; 72.11; 80.68; 28.90; 23.52; 53.28; 29.28; 25.22; 36.53; 25.92; 28.42; 20.70; 16.96; 16.13; 20.69; 20.85; 19.69; 18.37; 19.67; 17.61; 16.35; 18.13; 22.17; 17.19; 17.13; 18.45; 18.29; 17.91; 17.19; 21.47; 19.36];

t = [1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0; 0];

// Create the perceptron and Train it

[w,b] = ann_PERCEPTRON(p,t);

// To test the performance, simulate the perceptron with p

y = ann_PERCEPTRON_run(p,w,b)

for this code it show me the

weight (w) = 50 x 50 value

bias (b) = 50 value

i have 50 value of input (p) and 50 value of output(t).

what i just need is 50 value of weight (w) and only 1 value of bias (b).

can anybody here’s help me….

(sorry my bad english)

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Hello…

Can anybody tell me the code for perceptron model for sum of two numbers………

Why do you update weights always? They should be updated if input is not correctly classified.

I have a question about the first step that verify the parameter.

In that,

rand(‘state’,sum(100*clock));

weights = -1*2.*rand(3,1); are given

but I dont understand what means of

rand(‘state’,sum(100*clock));

In my opinion, parameter of weights are related with rand(3,1).

Also, rand(3,1) and rand(‘state’,sum(100*clock)) is independent, so I think rand(‘state’,sum(100*clock))

has no meaning in this code.

Is it right?

What means of rand(‘state’,sum(100*clock));.

hi dear

how can i implement a back propagation algorithm in matlab without neural network toolbox

and our input are matrices with variant dimension

more over a curve for error

best wish

hi dear

how can i implement a back propagation algorithm in matlab without neural network toolbox

and our input are matrices with variant dimension

more over a curve for error

best wish

hi dear

how can i implement a back propagation algorithm in matlab without neural network toolbox

and our input are matrices with variant dimension

more over a curve for error

best wish

%is this correct for any number of input and output units?

clc

%define the corresponding input and output values (must have the same

%amount of rows/patterns in each). Number of columns in each array are number of input

%and output units respectively

input = [1 1 1 0 0 0 1; 0 1 0 1 1 0 1; 0 1 1 0 1 1 0; 0 0 1 1 0 1 1;0 0 1 1 1 1 0; 0 1 0 0 1 1 1]

desired_out = [1 0 0 0 0 0;0 1 0 0 0 0;0 0 1 0 0 0;0 0 0 1 0 0;0 0 0 0 1 0;0 0 0 0 0 1];

%define number of units

[numPat numIn] = size(input); %number of patterns (numPat) and number of input units (numIn)

[r numOut]=size(desired_out); %number of output units (numOut)

%~~~~~~~~~~~~~~~~~~~~~~~~~~~~

%set constants

bias = -1;

coeff = 0.7;

%initiallise weights

rand(‘state’,sum(100*clock));

a = -2; %min random weight

b= 2; %max random weight

weights = a + (b-a).*rand(numIn,numOut+1);

iterations = 200;

for i = 1:iterations

out = zeros(numPat,numOut);

for j = 1:numPat %for each pattern

for k = 1:numOut %for each output

%sum all of the inputs (L) tot the output (k) for this pattern(j)

y=bias*weights(k,numOut+1); %put bias in last weight slot. The bias is the innate bias of an output unit (k)

for L = 1:numIn %for each input unit

y = y+input(j,L)*weights(L,k); %cycle through the inputs (L) for the current pattern (j)

end

%get the delta eror for the output unit (the output should

%match the desired output)

out(j,k) = 1/(1+exp(-y));

delta = desired_out(j,k)-out(j,k);

%adjust the weights

for L=1:numIn

weights(L,k)=weights(L,k)+coeff*input(j,L)*delta; %adjust weigh tfrom input (L) to output (k) for pattern (j)

end

weights(k,numOut+1)=weights(k,numOut+1)+coeff*bias*delta; %adjust the bias for this output

end

end

end

weights

out

i think you should swap iterators of numPat and numIn.

in fact, learning process refines weights for each feature of samples.

This is my code

clear

clc

Class = [1 1 2 2];

Agment = 1;

V = [0;0;0];

C = 1;

X1 = [Agment;2;1];

X2 = [Agment;2;5];

X3 = [Agment;5;2];

X4 = [Agment;4;6];

X = [X1 X2 X3 X4];

for I = 1 : size(X,2)

if Class(1, I) == 2

X(:, I) = X(:, I) * (-1);

end

end

K = 1;

for Step = 1 : 100

t=(V’ * X(:, K));

if (V’ * X(:, K)) <= 0

V = V + (C * X(:, K));

end

K = mod(K, size(X,2));

K = K + 1;

end

Hello, i am new on Matlab, where i can find the neural network toolbox so i can import it on mu R2012b Matlab

Thanks!

It’s usuful

now i want to enter only 2 bit input and get the answer what should i do?? imean the input is a matrix and i want the i/ps only 2 bit [0 1] or[1 0] or [0 0] or[1 1]

%here it is, it works well

clear out

test=[1 1];

y = bias*weights(1,1)+…

test(1,1)*weights(2,1)+test(1,2)*weights(3,1);

out = 1/(1+exp(-y))

hi

i am just the beginner using the nn tool in matlab….the first question is when we have to give both inputs and outputs to the nn gui tool in feedforward algorithm then for what we are using it…..i mean if we have output then why do we need to use that tool….please someone help me…i am not able to understand anything…..PLZZZZZZZZZZZZZZZ

neural network is a kind of thing that learn from experience. So by giving inputs and outputs we are train’n it to recognize another input which is similar in pattern we trained. In here, by giving inputs and outputs we are train’n the network.

Hi,

Could you please tell how to implement perceptron when we have an image of size(say 50×50) as input and more than one output(say 5 options)..

Question again why are there 3 weights? and i tried solving the weight output but it doesnt give me the correct answer like

input 1 = 1;

input 2 = 1;

desired output = 1;

the weight given to me by the code after i executed the code is

36.2816

24.0756

24.0756

so,

1×36.2816 = 36.2816

1×24.0756 = 24.0756

Bias = -1

36.2816+24.0756+(-1) = 59.3572

why is the answer not 1?

why is the rule for perceptron not included here?

the three rules where can i find it in this code?

the classify Xk part

https://www.cs.washington.edu/education/courses/415/04wi/slides/Neural/sld009.htm

how to load excel chart 4×18 in percepton with nntool??

tenks

how can i solve a differential equation using neural network scheem in matlab

suppose my equation is dy/dx = 3*sin(x)+e^2x

Pingback: Neural Networks – A perceptron in Matlab – PIYABUTE FUANGKHON

hi all,

I have a question and i really need help coz I’ve tried everything but in vain.

i have a matrix A = [k x 1] where k = 10,000 and ranging, say, from a to b, randomly.

I need to get a matrix B = [m x 1] from A, where m is from a to c ( a<c<b),…

basically, i want to do is to "shrink" A and get a smaller matrix B.

Thanks everybody.

Nietzsche.

From your question, I’m assuming something like the following?:

% Preallocate a random 10000 x 1 matrix A

A = rand(10000,1);

a = min(A)

b = max(A)

% set c = to a random value in between a and b. Lets choose 0.5 for this example

c = 0.5

% Then create B for between a and b

B = A(A< =c) % Can also use the following, though the second part is redundant in this case B = A(A<=c & A>=a)

hi

thanks for your tutorial could you please explain how to solve differential equation in neural networks

Regards

Deepika,

We’ll try to do something on this in the near future. Thanks,

Vipul

should’t the input entered be:

input = [0 0; 1 0; 0 1; 1 1];

Instead of….

input = [0 0; 0 1; 1 0; 1 1];

I’m sure I’m just confused but I need to use the following input data (and am uncertain about how to enter it):

X1=0, 0, 1, 1

X2=0, 1, 0, 1

would it be

input = [0 0;0 1; 1 0; 1 1]

or

input = [0 0;1 0;0 1;1 1]

Your help would be much appreciated

You are correct. In our example here for OR, both [1 0] and [0 1] map to an output of 1 though, so it works still.

If you have a matrix of inputs = [X1 X2] which are defined as follows:

X1=0, 0, 1, 1

X2=0, 1, 0, 1

Then you would use this:

input = [0 0;0 1; 1 0; 1 1]

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Hello

I’ve tried this example. I always get same results:

Out

0.5

0.5

0.5

0.5

weights:

0

0

0

I don’t know what is wrong with my code. please help. here is my code

input =[0 0; 0 1; 1 0; 1 1];

numIn = 4;

desired_out = [0;1;1;1];

bias = -1;

coeff = 1;

%rand(‘state’, sum(100*clock));

weights = -1*2.*rand(3,1);

iterations = 10000;

for i =1:iterations

out = zeros(4,1);

for j=1:numIn

y = bias*weights(1,1)+…

input(j,1)*weights(2,1)+input(j,2)*weights(3,1);

out(j) = 1/(1+exp(-y));

delta=desired_out(j)-out(j);

weights(1,1)=weights(1,1)*coeff*bias*delta;

weights(2,1)=weights(2,1)*coeff*input(j,1)*delta;

weights(3,1)=weights(3,1)*coeff*input(j,2)*delta;

end

end

solved

hi, thanks for the good explaination about perceptron.

I hv one question, this program is to train the input right??

then..how i’m going to test the input for classification using perceptron ?

David,

I don’t know if I follow your question. You could plot the results, residuals, MSE errors or other variables over each iteration. If you want to do something like this, that would be possible. If this isn’t what you were looking for, let me know.

Vipul

sorry for my english

how I can plot this perceptron?

Thanks.

Saeed,

An implementation of a multilayer perceptron is now available.

http://matlabgeeks.com/tips-tutorials/neural-networks-a-multilayer-perceptron-in-matlab/

Take care,

Vipul

hi

thank you for having this brief and useful tutorial.

I’d really appreciate if you send me a multilayer perceptron implementation using matlab .

best regards.