A neural network based on an inexpensive eight-bit microcontroller

Abstract

In this paper, a neural network is trained and validated using a low end and inexpensive microcontroller. The well-known backpropagation algorithm is implemented to train a neural network model. Both the training and the validation parts are shown through an alphanumeric liquid crystal display. A chemical process was chosen as a realistic nonlinear system to demonstrate the feasibility, and the performance of the results found using the microcontroller. A comparison was made between the microcontroller and the computer results.

Modeling pH Neutralization Process using Fuzzy Dynamic Neural Units Approaches

“…The model of the pH neutralization process studied in this work is a Continuous Stirred Tank Reactor (CSTR) proposed by McAvoy et al (1972) [28], and used in our previous work [29], when the full global neural network is implemented in an inexpensive microcontroller, contains two main parts, the first one is dynamic reaction between two inlet streams. The CSTR model is given by the following nonlinear dynamic equations:…”

Wavelet Network Implementation on an Inexpensive Eight Bit Microcontroller

“…In our previous paper (Saad Saoud & Khellaf, 2011) the neural network is implemented and trained using Microchip microcontroller (PIC16F876A). In this work, the extension of this card is used to train and validate another more powerful model which is the wavelet network.…”

“…We choose randomly a sequence of 750 samples of the input-output from the whole given data. As we have made in our previous works (Saad Saoud & Khellaf, 2011;, before using the data, we have to make several changes to be accepted by the network. The used data saved in the EEPROM passed through two necessary operations, first it has been normalized to 0.1 and 0.9 and second it is multiplied by 36408 to cover the maximum range and not exceeding the positive signed range of 16 bit (7FFF hex) of data.…”

“…First, the assembly language line number is smaller in the wavelet network based card than the program based on the neural network. This difference gives the card an important consequence which is the time reduction 2.77 second which is greater in the card based on the neural network (Saad Saoud & Khellaf, 2011). These two important advantages are the result of the network architecture itself, because the wavelet network has a linear output which makes the calculations easier.…”

“…In several applications such as adaptive control (Plett, 2003), or predictive control (Liu et al, 1998), we need to adapt the network's parameters in real time and in this case the computer is very important to adapt the parameters. These problems were overcome with the implementation of the whole neural network with its backpropagation algorithm in the microcontroller, which is proposed in our previous work (Saad Saoud & Khellaf, 2011). In this later work a multilayer neural network is trained and validated using a very inexpensive and low end microcontroller, but the problems of larger dimensions still exist.…”