Abstract: This lecture will address the following specific questions and issues:
- What is computational engineering?
- What does “real-time” mean, and under which specific (industrial/practical) circumstances is a real-time computational engineering needed? Some selected examples from traffic telematics and machine vision will be provided
- How far do traditional and even some new high-performance computing paradigms fail to satisfy the hard requirements of real-time computational engineering?
- What is neuro-computing? And what are the major related issues?
- How far does neuro-computing offer a great potential to satisfy the hard requirements of real-time computational engineering?
- What are synergetic ties with other computational intelligence instruments like genetic algorithms, particle swarm optimization, etc.?
- Some illustrative neuro-computing examples involving cellular neural networks
- Hardware and software related implementation issues. Which inspiration does the old “analog computing” paradigm offer?
- OUTLOOK: Overview of some interesting case studies and other potential industrial, graph theory and computer engineering applications
Keywords: Neuro-computing, Realtime computational engineering, Cellular neural networks, High-performance computing paradigms, Complexity reduction
