Neural networks for financial applications

The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways:

Neural networks for financial applications

Neural networks for financial applications

Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered. Neurocomputing's Software Track allows you to expose your complete Software work to the community Read more Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing.

Engineering Applications of Artificial Intelligence

Neurocomputing's Software Track allows you to expose your complete Software work to the community through a novel Publication format: Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.

Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips digital, analog, optical, and biodevices.

Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting. Neurocomputing publishes reviews of literature about neurocomputing and affine fields.

Neurocomputing reports on meetings, including, but not restricted to, conferences, workshops and seminars.

Neural networks for financial applications

Neurocomputing Letters allow for the rapid publication of special short communications. The Neurocomputing Software Track Neurocomputing Software Track publishes a new format, the Original Software Publication OSP to disseminate exiting and useful software in the areas of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.

We encourage high-quality original software submissions which contain non-trivial contributions in the above areas related to the implementations of algorithms, toolboxes, and real systems.

Artificial neural network - Wikipedia

The software must adhere to a recognized legal license, such as OSI approved licenses. Importantly, the software will be a full peer reviewed publication that is able to capture your software updates once they are released.

See the detailed Submission instructions, and more information about the process for academically publishing your Software:Dec 14,  · The phrase “artificial intelligence” is invoked as if its meaning were self-evident, but it has always been a source of confusion and controversy.

Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders.5/5(1). Gately, in his book, Neural Networks for Financial Forecasting, describes the general methodology required to build, train, and test a neural network using commercially available software.

The paper discusses PHM as a principle that includes health assessment, prediction and management. • The research provides a comprehensive overview of PHM tools for critical machinery components. Neural Net The inputs Set separation Neural Network paradigms From a mathematical point of view, a neural network is a function f: RN → RM where the function f is defined as the composition of other function g.

Simulator for Neural Networks and Action Potentials: SNNAP -- Simulator for Neural Networks and Action Potentials is a tool for rapid development and simulation of realistic models of single neurons and small neural networks.

Neural Networks: Forecasting Profits