Automated implementation of rule-based expert systems with neural networks for time-critical applications

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed

Artificial Neural Network Application In Environmental Engineering.

The objective of this thesis research is to apply two artificial neural network (ANN) methods, back-propagation neural network (BPN) and radial basis function generalized regression neural network (RBFGRNN) in two environmental engineering case studies to explore their ability to modeling the complex environmental engineering systems. The traditional environmental engineering systems modeling are frequently using the physical-based modeling methods

Development of a heat transfer and artificial neural networks teaching laboratory practical for biotechnology students

The paper describes a newly developed laboratory practical that teaches students how to develop an Artificial Neural Network model and its possible use in bio-processing. An emphasis is placed on giving students "hands on" experience with bio-processing equipment, namely bio-reactors and data acquisition systems in an attempt to help prepare them for work in bio-processing and chemical engineering industries

Guest Editorial

AFTER the tremendous clinical success of the cochlear implant over the last 20 years, neuroprosthetic systems are now being developed and applied for the blind. First results on implanted epiretinal arrays in humans are becoming available now and lead to clear suggestions of how to improve electrode design, device characteristics, and implant procedures. Besides implants in humans and animals, research on in vitro neuronal network systems is progressively expanding. Interesting combinations of multi-electrode array devices with microfluidic systems will allow pharmacological control of networks in a very precise way. Several papers in this Special Issue of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING are related to various neural neuroprosthetic systems. This Special Issue is a collective effort by active researchers who specialize in the field of neural engineering, and we hope it will provide a rich resource with regard to the state-of-the-art of neural engineering research

Neuromorphic engineering: neuromimetic computation for understanding the brain

Neuromorphic engineering attempts to understand the computational properties of neural processing systems by building electronic circuits and systems that emulate the principles of computation in the neural systems. The electronic systems that are developed in this process can serve both engineering and life sciences in various ways ranging from low-power brain-like computing embedded systems to neural-based control, brain machine interfaces, and neuroprosthesis. To realize such systems, various approaches and strategies with their own advantages and limitations, may be adopted. Here, we provide a summary of our recent article published in the proceedings of the IEEE [1], where we have discussed and reviewed the various approaches to the design and implementation of neuromorphic learning systems, and pointed out challenges and opportunities in these systems