Download Ph.D. Researcher, Slovenia

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.