In engineering practice we often have to deal with complex systems, where the conventional approaches for understanding and predicting the behavior of the system can prove to be inadequate. Hence, the researchers try to put some intelligence into the system. The term intelligence in this context still more or less remains a mysterious phenomenon and can be characterized by different abilities of the system or machine, such as adaptation, decision-making, learning, recognition, diagnostics, autonomy, etc. Many of the new results related to this area are published in Journals and in International Conference Proceedings. One such conference is the "IEEE International Conference on Intelligent Engineering Systems". The fourth conference in this series (INES 2000) took place in Portoroz, Slovenia, on September 17-19,2000. There were around eighty participants from eighteen countries around the world. We are glad that so many authors have contributed to ideas related to the issues at the conference. Many of the papers were about applications and design, and others on more theoretical aspects of intelligent systems. This variety made the selection of papers for this special issue very difficult. Eight papers have been selected in the end, which cover different aspects of intelligent engineering systems. It should be pointed out that the respective authors were also kind to revise and update the presented papers for this special issue. The first paper deals with the manipulation problem where the motion changes depending on the state of the system as it is the case in the finger gaiting applications. To solve it the semi-stratified control theory using smooth motion planning is used. The proposed concept combines the stratified motion planning with the unconstrained finger allocations. In the second paper a special branch of Soft Computing developed for the control of mechanical devices is described. It reduces the number of free parameters and computational complexity. For illustration of the efficiency of the proposed adaptive control, a simulation of polishing with a 3 DOF robot is given. The next paper discusses the force control of redundant robots in an unstructured environment. A special attention is given to the decoupling of the task space and null space motion. For that the minimal null space approach is used. The proposed impedance controller assures good task space performances and minimizes the disturbances caused by obstacles. The performance of the proposed controllers has been evaluated by the simulation and by experiments on a real robot. The forth paper presents some advanced modeling approaches and methods. As one of the key issues a manufacturing process model fully associative with form feature based part model has been introduced. The motivation has been that the low level integration of design and manufacturing of mechanical parts, as identified by the authors, is still a main drawback of efficient application of expensive modeling systems. The proposed method allows for creating part model simultaneously with their analysis of machineability. The next paper discusses the design of fractal-order discrete-time controllers. Some approaches to implement fractal derivatives and integrals are analyzed. As the application of the theory of fractional calculus is rather new, many aspects remain to be investigated. The sixth paper demonstrates how to map classical dictionaries and similar structured data to a hypertext structure that is more suitable for the modern media. To achieve the new shape automatically, the HiLog language is used. The automated mapping is illustrated by an example based on Oxford Dictionary of Modern English. In the seventh paper a humanoid robotics shoulder is compared to the human shoulder. First, the capabilities of the robotics shoulder are analyzed and next, using the optical measurement system the human shoulder movements have been measured and analyzed. The last paper discusses the bias-variance tests on multi-layer perception. The performance of Bayesian neural networks is compared with the performance of neural networks trained with a gradient method. Additionally, it is analyzed if it is possible to use a number of networks in committee trained with gradient descent to achieve the performance of a Bayesian network.
Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant
