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An intelligent system works on artificial intelligence. It is a most demanding concept of modern world. Performance specifications and system outputs are two major concept in intelligent systems while control is the heart of engineering system as it integrates the plants with actuators, sensors, computers and communications to produce optimal responses. Intelligent systems are usually applicable in following listed fields:

Robotics
Automated manufacturing
Precision engineering
Process industries
Aerospace and Automotive industries
Visual inspection
Medical care
Assistive robotics
Character recognition
Visual surveillance
Field and service robotics

Intelligent systems are advanced machines which perceive and respond to the world. It involves Roomba, automated vacuums, facial recognition programs, shopping suggestions etc. Beside this, it involves some challenges which are listed below:

Dynamic world: it requires decisions to be made at fast time scales to the changing physical world.

Uncertainty: a system may take incorrect inputs due to noise and other limitations in executing actions.

Mapping: sometimes it happens that information might get lost while transforming from the 3D world to the 2D world.

Time-consuming computation: an optimal path searching consumes time as it requires extensive search through a very large state space which is expensive too.

Intelligent systems incorporate intelligence into machines which performs search and optimization with intelligent learning.

Intelligent systems are the information systems that can make decisions by themselves. There are various examples that include web applications and medical uses. Major categories of intelligent systems include expert systems, neural networks, fuzzy logic, genetic algorithms, intelligent agents etc.
Intelligence control is a class of control techniques that makes the use of various artificial intelligence computing approaches such as Bayesian probability, neural networks, machine learning, fuzzy logic, genetic algorithms and evolutionary computation.
Neural networks : In machine learning and cognitive science, artificial neural networks (ANNs) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) which are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.
Bayesian control : Bayesian probability is one interpretation of the concept of probability. In contrast to interpreting probability as frequency or propensity of some phenomenon, Bayesian probability is a quantity that is assigned to represent a state of knowledge, or a state of belief.
Fuzzy logic : A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).
The major courses to study in Intelligent Systems and Control are Artificial neural networks, Back-propagation networks, Radial basis function networks, and recurrent networks, Fuzzy logic, knowledge representation and inference mechanism, genetic algorithm, and fuzzy neural networks, Fuzzy and expert control etc.

An intelligent system can be defined as a machine that has an embedded and internet-connected computer that is capable of collecting & analyzing data and communicating with other systems. An intelligent system requires security, the capacity for the purpose of remote monitoring as well as management, connectivity and the capability for adapting in accordance with the current data. An intelligent system may be differentiated on the basis of many dimensions. There are intelligence levels or degrees that may be measured using different various intelligence dimensions.

At the lowest level, intelligence needs capability for sensing the environment, for making decisions as well as for controlling action. At the higher levels, intelligence needs to be capable of recognizing the objects as well as events, for representing knowledge in a world model & for giving reason regarding & future plans. In the advanced forms or at the highest level, intelligence needs to be capable of perceiving as well as understanding, for selecting wisely & for acting successfully under many different circumstances in order to survive & prosper in a hostile as well as complex environment. In the intelligent systems the concepts of control & intelligence are strongly related. An intelligent system has to define & use objectives. After that control is needed to move the system to these defined objectives & also for defining such objectives.

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Topics for Intelligent Systems and Control   Assignment  help : 

  • Implementation of intelligent computational systems
  • Evaluation of intelligent computational systems
  • Artificial intelligence to natural language image understanding

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  • Problem analysis
  • Problem feature analysis
  • Information/data analysis
  • Viability analysis
  • Economical analysis
  • Environmental and sustainability analysis
  • Intelligent system project issues
  • Sub-goals
  • Task analysis
  • Data/information extraction
  • Data mining
  • Knowledge acquisition process
  • Knowledge/ontological analysis
  • Planning and selection of intelligent/statistical techniques
  • Construction of models
  • Implementation of techniques
  • Module integration
  • Validation of models/techniques
  • Comparison of techniques
  • Proposed solution
  • Intelligent system project output
  • Project system documentation
  • System manual
  • Intelligent methods and models
  • Software tools
  • AI
  • Computational metaphor
  • Church-turing thesis
  • Turing test
  • Calculus
  • Differentiation
  • Newton's method
  • Partial differentiation
  • Integration
  • Standard integrals
  • Integration by parts
  • Numerical integration
  • Satisfactory paths
  • Depth-first and breadth-first
  • Iterative deepening
  • Evolutionary algorithms
  • Hill-climbing and gradient descent
  • Beam search
  • Branch and bound
  • Dynamic programming
  • Representing knowledge
  • Monotonic and non-monotonic logics
  • Semantic nets
  • Frames and scripts
  • Reasoning and control
  • Data-driven reasoning
  • Goal-driven reasoning
  • And/or graphs
  • Truth-maintenance systems
  • Abduction and uncertainty
  • Reasoning under uncertainty
  • Probabilities
  • Bayesian networks
  • Noisy-or, d-separation
  • Belief propagation
  • Machine learning
  • Inductive and deductive learning
  • Unsupervised and supervised learning
  • Reinforcement learning
  • Classification and regression trees
  • Bayesian methods
  • Expert system
  • Decision support systems
  • Natural language processing
  • Information retrieval
  • Semantic web

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