solution manual to introduction to artificial neural systems by zurada

A Basic Introduction To Neural Networks


What Is A Neural Network?

The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen. He defines a neural network as:
"...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.
In "Neural Network Primer: Part I" by Maureen Caudill, AI Expert, Feb. 1989

ANNs are processing devices (algorithms or actual hardware) that are loosely modeled after the neuronal structure of the mamalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mamalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior. Although ANN researchers are generally not concerned with whether their networks accurately resemble biological systems, some have. For example, researchers have accurately simulated the function of the retina and modeled the eye rather well.
Although the mathematics involved with neural networking is not a trivial matter, a user can rather easily gain at least an operational understanding of their structure and function.

A basic introduction to neural networks


What is a neural network?

A neural network is a simple definition, and more properly to an "artificial neural network (ANN), ' one of the first neurocomputers Dr. Robert Hecht-Nielsen is provided by the inventor of the referred to. He said that as a neural network defines:
"... A computing system is simple, has a number of highly interconnected processing elements, external inputs for their dynamic State feedback is information about the process.
Maureen Caudill, AI Expert, February 1989: "part I neural network Primer" in

ANNs tools (algorithms or the actual hardware) that loosely modeled after neuronal structure of cerebral cortex mamalian but on much smaller scales are processed. While a mamalian magnitude of their overall brain interact and casual behavior with a corresponding increase in the billions of neurons is a large n, may be hundreds or thousands of processor units. Although ANN researchers generally with your network to accurately resemble biological systems that do not have some people worried. For example, the researchers right retina function fake and rather well eye is modelled.
Although mathematics Neural Networking is not a small thing with, easily a user at least understand their structure and can achieve an operational function.