History[ edit ] Warren McCulloch and Walter Pitts  created a computational model for neural networks based on mathematics and algorithms called threshold logic. This model paved the way for neural network research to split into two approaches. One approach focused on biological processes in the brain while the other focused on the application of neural networks to artificial intelligence. This work led to work on nerve networks and their link to finite automata.
Artificial neural networks Artificial neural networks Neural computing has emerged as a practical technology in recent years, with successful applications in many fields as diverse as medicine, finance, geology, physics, engineering and biology. The excitement ranges from the fact that these networks are attempts to model the capabilities of the man brain.
From a statistical point of view neural networks are interesting because of their potential use in classification problems and prediction . Artificial neural networks ANNs are non-linear data driven self-adaptive approach as opposed to the traditional model based methods.
Neural networks are powerful tools used for modeling especially when the underlying data relationship is unknown. NNs can identify and learn correlated patterns between input data sets and corresponding target values.
After training and testing, NNs can be used to predict the outcome of new independent input data . NNs imitates the learning process of the man brain and can process problems involving complex data and non-linear even if the data are and noisy imprecise.
They are ideally suited for the modeling of agricultural data which are known to be complex and often non linear . Artificial neural networks ANNs have been used for a wide variety of applications where statistical methods are traditionally being employed .
They have been used in Artificial neural networks essay as predicting the secondary structure of globular proteins classification problems, identifying underwater sonar currents, and recognizing speech and pattern recognition.
In time series applications, NNs have been used in predicting stock market performance . As users of statistics or statisticians the problems are normally solved through classical statistical methods, such as Bayes analysis, discriminant analysis, logistic regression, and ARIMA time series models and multiple regression.
Therefore, it is time to recognize neural networks as a powerful tool for data analysis . An artificial neural network consists of an interconnected group of artificial neurons. In almost all cases an ANN is an adaptive system that changes its structure based on internal information or external information that flows through the network during the learning phase.
And the Modern neural networks are usually used to model complex relationships between outputs and inputs or to find patterns in data . Neural networks have been used in building an artificial intelligent information system that ape the way in which humans think .
Thus they therefore can, identify new objects previously untrained. We will specifically be looking at training single-layer perceptron with the perceptron-learning rule.
Before we begin, we should probably first define what we mean by the word learning in the context of this chapter.
It is still unclear whether machines will ever be able to learn in the sense that they will have some kind of metacognition about what they are learning like humans . However, they can learn how to perform tasks better from past experiences.
You may recall from the previous chapters that neural networks are inspired by the biological nervous system, in particular, the human brain. We should note that our understanding of how exactly the brain does this is still very primitive, although we still have a basic understanding of the process .
Although simplified, artificial neural networks can model this learning process by adjusting the weighted connections found between neurons in the network. This effectively imitate the weakening and strengthening of the synaptic connections found in our brains. The strengthening and weakening of connections is what will enable the network to learn .
Facial recognition will be a very example of a problem extremely hard for a human to accurately convert into code . Problem that could be solved better by a learning algorithm would be a loan granting application, which can use past loan data to classify future loan applications.
Although human beings could write rules to do this a learning algorithm can better pick up on subtleties in the data that may be hard to code for .
Learning is a continuous classification process of input stimuli when a stimulus appears at the network the network either it develops or recognizes a new classification.NEURAL NETWORKS Neural Networks Affiliation This paper presents an overview of the Artificial Neural Networks (ANNs).
This paper will outline the basic idea, some history and uses of ANN. Basically an artificial neural network is a collection of programs of data structures that is almost capable of the process of the human brain.
The History Of Artificial Neural Networks Information Technology Essay.
D:\download \Downloads\images (20).jpg. Neural Networks have tree-like networks of nerve fibers called dendrites are connected to the cell body or soma, where the cell nucleus is located.
Artificial neural network (ANN),which are also usually called neural network (NN), is a computational model or mathematical model that is inspired by the structure and/or .
ARTIFICIAL NEURAL NETWORKS: TERMINOLOGY Processing Unit: We can consider an artificial neural network (ANN) as a highly simplified model of a structure of the biological neural network.
ANN consists of interconnected processing units. The general model of a processing unit consists of summing part followed by an output part. This free Information Technology essay on Artificial neural networks is perfect for Information Technology students to use as an example.
Neural networks in medicine Artificial Neural Networks (ANN) are currently a ‘hot’ research area in medicine and it is believed that they will receive extensive .