Introduction of Intelligent System

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The domain of Intelligent Systems (IS) has extended exceptionally across the years following the 1940s, both as far as the scope of strategies and furthermore as far as the number of applications wherein they have often provided a competitive edge when compared with others approaches.

An intelligent system is an embedded, Internet-connected computer that has the capacity to collect and analyze data and interact with other systems. Intelligent systems are technologically advanced machines that perceive and respond to the world around them. It includes the capacity to learn from experience, security, connectivity, the ability to adapt according to current data and the capacity for remote monitoring and management.

An intelligent system is not only adaptive, self-learning, fault-tolerant, self-organized & self-repairing at every level of the hierarchy, but also capable of dealing with uncertainty
The major categories of IS include neural networks (NNs), fuzzy logic/systems (FL/Ss), evolutionary computation/algorithms (EC/As) (including genetic algorithms (GAs), genetic programming (GP), evolutionary strategies (ES)), support vector machines (SVM), particle swarm optimization (PSO), memetic algorithms (MAs), and ant colony optimization (ACO).

 

Features of Intelligent Systems:

One of the necessary conditions for a system to be Intelligent is that it must be able to interpret information, comprehend the relations between the phenomena or objects, perform meaningful operations & can apply the acquired information to changing the set of conditions. A typical intelligent system must have the following features:
  1. Fault-tolerant
  2. Self-correcting
  3. Adaptive
  4. Self- organizing
  5. Robust
  6. Mobile and Distributed
  7. Seamless Integration
  8. Networked
  9. Validation and certification

Challenges of Intelligent Systems:

Research in intelligent systems faces numerous challenges, many of which relate to representing a dynamic physical world computationally.

  1. Uncertainty: Physical sensors/effectors provide limited, noisy and inadequate information/action. Therefore, any actions the system takes may be incorrect both due to noise in the sensors and due to the limitations in executing those actions.
  2. The dynamic world: The physical world changes continuously, requiring that decisions be made at fast time scales to accommodate the changes in the environment.
  3. Time-consuming computation: Searching for the optimal path to a goal requires an extensive search through a very large state space, which is computationally expensive. The drawback of spending too much time on computation is that the world may change in the meantime, thus rendering the computed plan obsolete.
  4. Mapping: A lot of information is lost in the transformation from the 3D world to the 2D world. Computer vision must deal with challenges including changes in perspective, lighting, and scale; background clutter or motion; and grouping items with intra/inter-class variation.
  5. Reliability
  6. Safety of critical computing units
  7. Lack of intelligent operating systems
  8. Design, testing, certification are very expensive
  9. Heterogeneous systems

Applications areas of Intelligent Systems:

  • Factory automation
  • Field and service robotics
  • Assistive robotics
  • Military applications
  • Medical care
  • Education
  • Entertainment
  • Visual inspection
  • Character recognition
  • Human identification using various biometric modalities (e.g. face, fingerprint, iris, hand)
  • Visual surveillance
  • Intelligent transportation
  • Space
  • Telephone

 

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