Neural computing is an emerging research topic today due to its massive increase in demand and applications for machine learning. In this virtual simulation research work, using a free software, a program has been trained a neural network model and translate its functionality into the hardware. In the context of analog neural network, this research seeks to verify a shift sigmoid function that can approximate the transfer function of CMOS inverter. By showing this approximation accurately and reducing the number of components, it would help to implement the neural network based integrated chips. A conciliation is selected for the distance matric of the proposed function. This distance metric between the given CMOS transfer function and the shifted sigmoid function is minimized using the gradient descent. However, this approximate transfer function of CMOS inverter is chosen to verify in a three-layer perceptron networks. The network topology randomly generates weights to provide a diverse set of truth tables. We report two networks whose weights are chosen randomly using a back propagation algorithm due to volatile nature of the network topology and the activation function. The results of this research conclude that the transfer function of CMOS inverter is able to approximate the CMOS transfer function adequately for the purposes of these perceptron networks.

With the advent of memristors, analog artificial neural networks are closer than ever. Neural computing is growing as a topic of research. In the context of analog artificial neural networks, the purpose of this research is to verify that a perceptron could gain a discrete memory from implementing a hysteresis loop in the activation function. The discrete memory is represented by the difference path of the hysteresis activation function that took from logic 1 to logic 0. To write to the memory, the input to the hysteresis loop would have to exceed threshold. To read the stored value, the input would have to be between the thresholds of the hysteresis function. In order to verify the perceptron's memory, a network with manually chosen weights is selected which acts as a shift register. The components of this network are assembled in a circuit simulation program. Functionally, the network receives two inputs: a data signal and an enable signal. The output of the network is a time-shifted version of previous input signals. A system whose output is a time-shifted version of the previous inputs is considered to have memory.