Artificial Neural Network Abstract
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1) Artificial neural networks (ANNs) are processing systems inspired by biological neural networks, consisting of interconnected nodes that process information via algorithms or hardware components. ANNs can accurately model functions like visual processing in the retina. 2) ANNs are useful for problems like facial recognition that are difficult to solve with algorithms due to their ability to learn from examples in a way similar to the human brain. 3) ANNs have many applications, including pattern recognition, modeling complex relationships in large datasets, and real-time systems due to their parallel architecture.
Artificial Neural Network Abstract
- 1. ABSTRACT- ARTIFICIAL NEURAL NETWORK INTRODUCTION 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. ANNs are processing devices (algorithms or actual hardware) that are loosely modeled after the neuronal structure of the mammalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mammalian 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. PROBLEM STATEMENT Computers are great at solving algorithmic and math problems, but often the world can't easily be defined with a mathematical algorithm. Facial recognition and language processing are a couple of examples of problems that can't easily be quantified into an algorithm, however these tasks are trivial to humans. The key to Artificial Neural Networks is that their design enables them to process information in a similar way to our own biological brains, by drawing inspiration from how our own nervous system functions. This makes them useful tools for solving problems like facial recognition, which our biological brains can do easily. METHODS Neural networks can be hardware- (neurons are represented by physical components) or software-based (computer models), and can use a variety of topologies and learning algorithms. 1. Feedforward neural network 2. Radial basis function (RBF) network 3. Kohonen self-organizing network 4. Learning vector quantization 5. Recurrent neural network a. Fully recurrent network i. Hopfield network ii. Boltzmann machine b. Simple recurrent networks c. Echo state network d. Long short term memory network e. Bi-directional RNN f. Hierarchical RNN g. Stochastic neural networks 6. Modular neural networks a. Committee of machines i. Associative neural network (ASNN) 7. Physical neural network 8. Other types of networks a. Holographic associative memory b. Instantaneously trained networks c. Spiking neural networks d. Dynamic neural networks e. Cascading neural networks f. Neuro-fuzzy networks g. Compositional pattern-producing networks h. One-shot associative memory i. Hierarchical temporal memory
- 2. APPLICATIONS Neural networks are universal approximators, and they work best if the system you are using them to model has a high tolerance to error. One would therefore not be advised to use a neural network to balance one's cheque book! However they work very well for: Capturing associations or discovering regularities within a set of patterns; Where the volume, number of variables or diversity of the data is very great; The relationships between variables are vaguely understood; or, The relationships are difficult to describe adequately with conventional approaches. CONCLUSION The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture. Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain. Perhaps the most exciting aspect of neural networks is the possibility that someday 'conscious' networks might be produced. There is a number of scientists arguing that consciousness is a 'mechanical' property and that 'conscious' neural networks are a realistic possibility. Finally, we can say that even though neural networks have a huge potential we will only get the best of them when they are integrated with computing, AI, fuzzy logic and related subjects.