ARTIFICIAL INTELLIGENCE

 What Does Unification Mean? In computer science and logic, unification is the algorithmic procedure used in solving equations involving symbolic expressions. In other words, by replacing certain sub-expression variables with other expressions, unification tries to identify two symbolic expressions. Unification is used in automated reasoning technology, which remains one of the major application areas of unification. Unification is used in implementations such as: Programming language-type system implementation Logic programming SMT solvers Cryptographic protocol analysis Term-rewriting algorithms Unification is one of the fundamental techniques upon which methods for automated deduction are based.   Unification The term “unification” and its notion can be attributed to John Alan Robinson. He used unification as the basic operation of his resolution principle and also showed that unifiable terms have at most one general unifier. Several frameworks of unification are differen

Comparison Study of AI-based Methods in Wind Energy  https://www.intechopen.com/chapters/51398   Fuzzy logic control for switched reluctance motor drive https://ieeexplore.ieee.org/document/1176727     Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example  https://www.intechopen.com/chapters/50673   Retrieval of Optical Constant and Particle Size Distribution of Particulate Media Using the PSO-Based Neural Network   https://www.intechopen.com/chapters/50071     A Novel Artificial Organic Controller with Hermite Optical Flow Feedback for Mobile Robot Navigation https://www.intechopen.com/chapters/50313   Artificial intelligence and cognitive computing   https://www.i-scoop.eu/ai-artificial-intelligence-cognitive-computing/   Data Warehouse in the Age of AI Maturity   https://www.singlestore.com/blog/memsql-maturity-framework/   Artificial Intelligence in Medical Diagnosis   https://www.acpjournals.org/doi/abs/10.7326/0003-4819-108-1-80    

 Intelligent System for Controlling a Three-Phase Active Filter The nonlinear elements contained by electrothermal installation lead to three main negative effects, which are reflected in the power quality: circulation of reactive power, appearance of harmonic currents and unbalance of the power supply system.The problems that appear in power quality are due to the functioning of static electronic converters, power electronic devices, arc furnaces or fluorescent lamps. The frequency converters, which come in the electric scheme of the electrothermal installation, develop unfavourable effects into the power system: distortion regime generated by the voltage or current waveform, additional heating developed by the high value of the effective currents, or cable insulation damage. These problems can lead to malfunctioning of other electric devices, which are supplied from the same power distribution and also a quality diminution in electric power delivering [1]. Description of the intellig

  Iterated Gradient Descent Technique Gradient descent, also known as the steepest descent, is an iterative optimization algorithm to find a local minimum of a function. While minimizing the function, we are concerned with the cost or error to be minimized R e m e m b e r T r a v e l l i n g S a l e s m a n P r o b l e m . It is extensively used in deep learning, which is useful in a wide variety of situations. The point here to be remembered is that we are concerned with local optimization and not global optimization. Main Working Idea We can understand the main working idea of gradient descent with the help of the following steps − First, start with an initial guess of the solution. Then, take the gradient of the function at that point. Later, repeat the process by stepping the solution in the negative direction of the gradient. By following the above steps, the algorithm will eventually converge where the gradient is zero. Mathematical Concept Suppose we have a functio

  These are stochastic learning processes having recurrent structure and are the basis of the early optimization techniques used in ANN. Boltzmann Machine was invented by Geoffrey Hinton and Terry Sejnowski in 1985. More clarity can be observed in the words of Hinton on Boltzmann Machine. “A surprising feature of this network is that it uses only locally available information. The change of weight depends only on the behavior of the two units it connects, even though the change optimizes a global measure” - Ackley, Hinton 1985. Some important points about Boltzmann Machine − They use recurrent structure. They consist of stochastic neurons, which have one of the two possible states, either 1 or 0. Some of the neurons in this are adaptive f r e e s t a t e and some are clamped f r o z e n s t a t e If we apply simulated annealing on discrete Hopfield network, then it would become Boltzmann Machine. Objective of Boltzmann Machine The main purpose of Boltzmann Machine is to op