By Deyi Li, Yi Du
Via mathematical theories, types, and experimental computations, this e-book explores the uncertainties of information and intelligence that ensue through the cognitive methods of people. It describes the cloud version, its uncertainties of randomness and fuzziness, and the correlation among them. The e-book additionally facilities on different actual tools for facts mining, resembling the information box and information discovery country house. furthermore, it offers an inverted pendulum instance to debate reasoning and regulate with doubtful wisdom in addition to presents a cognitive physics version to imagine human pondering with hierarchy.
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Additional resources for Artificial Intelligence with Uncertainty
Each processing unit has a single output which, when necessary, can branch into a number of parallel connections with the same output signal. The output is independent of the number of branches. , depending only on the current value of all the input signals into the processing unit, the local value stored in the processing unit and its operations. The basic constituent parts of a connectionist structure can be activated to a certain degree under certain circumstances just like a simple unit of the brain neuron.
The main contributions of the Hopfield model are as follows: • • • Proposing the energy function for the system, which takes the feedback neural network as a nonlinear dynamic system for stabilizing analysis of the system. Using analog electronics circuits to construct a circuitry model for the Hopfield network. This model is best suited for solving combinatorial optimization problems like the TSP. 4 BACK-PROPAGATION NEURAL NETWORK MODEL Another typical structure of the connectionist approach is the back-propagation (BP) model,30 first proposed in 1974 by Paul J.
This relation is often nonlinear and characteristic of variable structure. The parameters are adjusted based on current deviation and deviation changing rate in terms of magnitude and direction and even the structure inside the system can be changed in the form of episodic change. Self-learning in intelligent control mainly concerns itself with the mathematical model of the system. Learning means receiving conditions and results from the outside. It is a process of knowledge accumulation, called training by some people.