Artificial Neural Network Advantages And Disadvantages

The following steps are used to design the back propagation neural network algorithm for the proposed research work. The first step is to set the input, output data sets. The second step is to set the number of hidden layer and output activation functions. The third step is to set the training functions and training parameters, finally run the network.

The training is divided into two phases: learning phase and testing phase. In the learning phase, an iterative which updated the synoptic weights is formed upon the error BP (Back Propagation) algorithm. In the testing phase the number of input and output parameters as well as the cases number influenced the neural network,whereas the trained results is then compared to the target to make a decision about the continuing of the iteration or the obtained results is concluded. The common ANN structure for the three architectures is (3X3), which means three neurons in the input layer and three neurons in the hidden layer. The training of each ANN architecture designs are shown in the following: fig.3, fig.4 and fig.5,

Information system can be categorized into two (2) categories which are manually and automatically. Usually, manual is a system where is all the information will be written and stored in different files. Manual system also known as non-computerized information system. It is using the old style that deals with papers and reports. However, the manual system in managing information is not an efficient method. It consumes a lot of time to finding and modifies the available information. In order to eliminate those issues, a new system is created which known as Computer-based Information System (CBIS). This is an information system that is based on computer hardware and software technology for processing and disseminating the information. The integration

The adversarial and inquisitorial trial systems are the two most widely used in the world, both operating in similar but different ways. The adversarial system is a system based on the notion of two adversaries battling in an arena before an impartial third party, with the emphasis on winning. There are five main elements of the adversarial system; the role of the judge, the role of the parties, the role of legal representation, burden and standard of proof and the rules of evidence of procedure. The inquisitorial system is a method of legal practice in which the judge endeavours to discover facts while simultaneously representing the interests of the state in a trial. In this system, the judge is primarily responsible for

Neural Network The idea envisaged for the neural network was that of a feed-forward neural network with a hidden layer. This was due to experience and familiarity with them from the Part IB ’Artificial Intelligence’ course. It was necessary to research neural network frameworks in order to

Simulated neural systems (ANNs) utilize a cartoon of the way the human mind forms data. An ANN has numerous preparing units (neurons or hubs) working as one. They are exceedingly interconnected by connections (neural connections) with weights. The system has an information layer, a yield layer and any number of shrouded layers. A neuron is connected to all neurons in the following layer (fig.1.2). ANNs are helpful in tackling information escalated issues where the calculation or principles to take care of the issue are obscure or hard to express. The information structure and non-direct calculations of ANNs permit great fits to perplexing, multivariable information. ANNs process data in parallel and are strong to information mistakes. They

Cancer is tumor disease that involves the uncontrolled growth of abnormal cell in any part of the body without being detected. Millions of deaths are being caused by this vicious disease per year. Not only is its diagnosis risky but its classification is an equally difficult challenge. In our task we have developed a breast classification model using multilayer neural network approach to address this problem. We have analyzed our model on an open source breast cancer dataset and have obtained promising results with the best accuracy of 99.5%.

The idea of neural computing grew out of desire to capture pattern recognition capabilities of a biological brain. Neural network usually presented as system interconnected ‘’neuron’’ that can compute values from inputs by feeding

One of this AI technologies tools is the Artificial Neural Networks which work much like the human brain and have the ability to learn from

We will refer to this grid as the input grid. Additionally, a SOM has a second grid, which, for this project, contains thirty-six randomized nodes close to the range of the data points. For us, each of the values is between zero and one. Each of these nodes is six dimensional in order to match our data points, so that now the thirty-six data points are located within the data space. We will refer to this grid as the map grid. Both of these sets of nodes are located in two separate matrixes. The two dimensional node and the six dimensional node located in the same row of the matrixes represent the same point. These randomized points relate to the ones in the static grid one-to-one, allowing us to keep the points in the randomized grid connected to one another in the same pattern that the nodes are connected in the static grid.

Multilayer Perceptron (MLP) is an artificial neural network that learns nonlinear function mappings. An MLP can be viewed as a logistic regression classifier where the input is first transformed using a learned non-linear transformation. This transformation projects the input data into space where it becomes linearly separable. This intermediate layer is referred to as a hidden layer. A single hidden layer is sufficient to make MLPs a universal approximator.

In[1], The techniques of artificial intelligence based on fuzzy logic and neural networks are applied together. The reasons to combine these two paradigms are because of the difficulties and inherent limitations of each isolated paradigm. They are called Neuro-Fuzzy Systems. This is often used to assign a specific type of system that integrates both of these techniques. This is characterized by a fuzzy system where fuzzy sets and rules are adjusted using input output patterns. There are many different implementations, where each author defined its own model. There is a general vision of the area describing the most hybrid neuro-fuzzy techniques, its pros and cons.

In undertaking the PGDipBE is an opportunity to gain perspective not only on the academic course, but also the way of life. It is a challenge in meeting different people with different cultures, language, personality and to be independent. Being diverse it promotes awareness and appreciates each other. Learning numerous life experiences in dealing with the tutors, classmates and the people who come across. Having class interactions and group work, understanding each other point of views helps in building camaraderie. After being together no more differences will be felt, discovering each other’s best potentials. More importantly, grows own personal characteristic to be patient, good listener and adaptable to the changes of the environment.

A.C 1.1 Discuss the benefits and constraints of different networking systems, types and topologies. Analyse and clearly identify the requirements of the system for this network in order to convince the management of the given case study company.

Soft computing techniques uses simple rules to model the system dynamics without knowing the mathematical model of the complex nonlinear system. Fuzzy controller is an intelligent controller based on the model of fuzzy logic that uses simple if-then rules and adds human intelligence so that the fuzzy controller does not require accurate mathematical modelling of the system. Neural networks,