英语翻译There are weights assigned with each arrow,which represe

英语翻译
There are weights assigned with each arrow,which represent information flow.These weights are multiplied by the values which go through each arrow,to give more or less strength to the signal which they transmit.The neurons of this network just sum their inputs.Since the input neurons have only one input,their output will be the input they received multiplied by a weight.What happens if this weight is negative?What happens if this weight is zero?
The neurons on the output layer receive the outputs of both input neurons,multiplied by their respective weights,and sum them.They give an output which is multiplied by another weight.
Now,set all the weights to be equal to one.This means that the information will flow unaffected.Compute the outputs of the network for the following inputs:(1,1),(1,0),(0,1),(0,0),(-1,1),(-1,-1).
Good.Now,choose weights among 0.5,0,and -0.5,and set them randomly along the network.Compute the outputs for the same inputs as above.Change some weights and see how the behaviour of the networks changes.Which weights are more critical (if you change those weights,the outputs will change more dramatically)?
Now,suppose we want a network like the one we are working with,such that the outputs should be the inputs in inverse order (e.g.(0.3,0.7)->(0.7,0.3)).
That was an easy one!Another easy network would be one where the outputs should be the double of the inputs.
Now,let’s set thresholds to the neurons.This is,if the previous output of the neuron (weighted sum of the inputs) is greater than the threshold of the neuron,the output of the neuron will be one,and zero otherwise.Set thresholds to a couple of the already developed networks,and see how this affects their behaviour.
Now,suppose we have a network which will receive for inputs only zeroes and/or ones.Adjust the weights and thresholds of the neurons so that the output of the first output neuron will be the conjunction (AND) of the network inputs (one when both inputs are one,zero otherwise),and the output of the second output neuron will be the disjunction (OR) of the network inputs (zero in both inputs are zeroes,one otherwise).You can see that there is more than one network which will give the requested result.
Now,perhaps it is not so complicated to adjust the weights of such a small network,but also the capabilities of this are quite limited.If we need a network of hundreds of neurons,how would you adjust the weights to obtain the desired output?There are methods for finding them,and now we will expose the most common one.