The Mathematical model Fixed input xo=± Once modeling an artificial functional model from 100—8080= bk(bias the biological neuron, we must take into account three basic components. First of all, the synapses of the biological neuron are modeled as weights. Let's n10-2wkI is the one which interconnects the neural networ remember that the synapse of the biological neuron Activation and gives the strength of the connection. For an Finction artificial neuron, the weight is a number, and 12O—用 k represents the synapse. A negative weight reflects an 卡9( Jk inhibitory connection, while positive values designate excitatory connections. The following components of the model represent the actual activity of the neuron cell. All inputs are summed altogether and modified by the weights. This activit k is referred as a linear combination. Finally, an Input Synaptic activation function controls the amplitude (it +o ) of Weights the output. For example, an acceptable range of output is usually between o and 1, or it could be-1 and l Once modeling an artificial functional model from the biological neuron, we must take into account three basic components. First of all, the synapses of the biological neuron are modeled as weights. Let’s remember that the synapse of the biological neuron is the one which interconnects the neural network and gives the strength of the connection. For an artificial neuron, the weight is a number, and represents the synapse. A negative weight reflects an inhibitory connection, while positive values designate excitatory connections. The following components of the model represent the actual activity of the neuron cell. All inputs are summed altogether and modified by the weights. This activity is referred as a linear combination. Finally, an activation function controls the amplitude (值幅)of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be -1 and 1