OF AN ARTIFICIAL NEURAL NETWORK FOR INSTRUMENTATION APPLICATIONS

One of the characteristics of many industrial processes is the complex interrelations among the variables of the process. This has lead to the development of 'software sensors' (softsensors or virtual sensors) where, by means of a computer program, variables are estimated from the information gathered by other measurements. We propose to synthesise the new 'sensor' in a CPLD to provide the process with a hardware version of the 'software sensor'. Our proposal is based on a modular design of a neuron using digital non-linear activation functions. The design methodology, the size and complexity of the synthesised hardware and the results of the tests in a real world problem are presented together with our conclusions on the viability for its wider use on instrumentation applications. INTRODUCTION. Within the monitoring and control tasks required to optimise process operation, on-line monitoring of all variables would be the best solution, as off-line methods mean loss of information density, delay in getting results and normally require greater human effort. Two barriers to the availability of on-line measurements for all process variables include absence of suitable detection methods, and the uneconomically high cost of some of the well known methods [1]. Hence, some required measurements in the process are determined by off-line analytical methods, although on-line measurements would be desirable. A hardware version of a neural-based sensor would be an important contribution towards fulfilling this need. The ability of Artificial Neural Networks (ANN) to learn from experience rather than from mechanistic descriptions is making them the preferred choice to model processes with intricate variable interrelations. Much work has been done on the area of simulations of ANN by means of a computer program but, although successful applications are now countless, the use of a dedicated personal computer is not always justified, especially when the application is not very complex. In these cases, a hardware implementation of the ANN solution would be desirable that eliminates part of the cost and gives better level of confidence in the product. We propose a design for the implementation of a ANN using the Complex Programmable Logic Device (CPLD) technology, based on previous successful attempts [2]. One of the characteristics of many industrial processes is the complex interrelation among the variables of the process. This has lead researchers to create 'software sensors' (soft-sensors or virtual sensors) whereby, by means of a computer program, variables are estimated from the information gathered by other measurements [3] [4] [5]. The new sensor will then be synthesised in a CPLD to provide the process with a hardware version of the 'software sensor'. The fusion of data and estimation with the above mentioned techniques at the simulation level has been successfully accomplished by previous work (Leal et al. [5]). We are making use of a software package to simulate and synthesise the design in a straightforward manner. Our aim here is to create a data processing application in a process using ANN through the implementation of the algorithms in the programmable logic device and thus bridge the gap left due to the lack of on-line measurements of the variables. We also make some conclusions about the size of the synthesised neuron and the viability to expand their use in instrumentation applications. DESCRIPTION OF THE CIRCUIT Conventional computer hardware is not optimised for neural network processing and, while their implicit functions are comparatively simple, their hardware implementation can be expensive. The following is the expression to be implemented in hardware, where, Ii are the input signals, Ri is the set of input vectors, Wji the weights and a the activation function. ( )       = ∑ ≤ ≤ j m j ji i m i I w a I I R * ,. ,