Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
New method of hybrid system using to predict topographical property and it’s application in Mechanical Engineering Matej Babič Ph.D. Researcher, Slovenia [email protected] Abstract Intelligent systems engineering is a blanket term used to refer to a variety of Artificial Intelligence (AI) approaches, including neural networks, evolutionary algorithms, model-based prediction and control, case-based diagnostic systems, conventional control theory, and symbolic AI. The term intelligent systems engineering is most frequently used in the context of AI applied to specific industrial challenges such as optimizing a process sequence in a sugar factory. Intelligent systems engineering tends to refer to the creation of short-term, narrowtask, marketable AI, rather than long-term, flexible, generally intelligent AI. Intelligent systems are usually meant to be coupled with robotics in industrial process settings, though they may be diagnostic systems connected only to passive sensors. Intelligent systems are meant to be adaptive, to solve problems as creatively as possible with minimal human input. The field has received substantial investment from both private sectors and the military. Intelligent systems generally follow a sequence of events in diagnosing and addressing a potential problem. First, the system identifies and defines the problem. Then it identifies evaluation criteria to apply to the situation, which it uses to generate a set of alternatives to the problem. We used an intelligent system method; namely, a neural network, genetic programming and regression for modelling results. In information technology, a neural network (NN) is a system of programs and data structures that approximates the operation of the human brain. Researchers from many scientific disciplines are designing artificial neural networks, to solve a variety of problems in pattern recognition, prediction, optimization, associative memory, and control. An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs. An artificial neural network (ANN) is a machine-learning approach that models the human brain and consists of a number of artificial neurons. Neurons in ANNs tend to have fewer connections than biological neurons. Each neuron in an ANN receives a number of inputs. An activation function is applied to these inputs, which results in activation level of neuron (output value of the neuron). Conventional approaches have been proposed for solving these problems. Fig. 1: The general multi-layer neural network system References [1] Graves, Alex; and Schmidhuber, Jürgen; Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, in Bengio, Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris K. I.; and Culotta, Aron (eds.), Advances in Neural Information Processing Systems 22 (NIPS'22), December 7th–10th, 2009, Vancouver, BC, Neural Information Processing Systems (NIPS) Foundation, 2009, pp. 545–552. [8] Koza, John (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.