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Hybrid systems and techniques, combining data-driven learning techniques (such as neural networks) with knowledge-driven techniques (such as fuzzy rules) begin to be extensively applied in the biomedical field, especially for signal analysis and interpretation and for computer-controlled systems. A real need for design and configuration of dedicated hybrid systems in modern surgery is arising: sensors, actuators, mechatronic systems and tools in minimally invasive surgery and microsurgery require non-linear interpretation and control systems to interface with the computer-assisted decisionmaking process of the surgeon at work. The design and the configuration techniques for real-time hybrid systems must fit the new technological advances in terms of specific (micro) sensors and multi-sensors and in terms of feed-back tool action-tissue reaction response and measurements.
Date of Conference: 2001