Neural networks are named after the cells in the human brain that perform intelligent operations. The brain is made up of billions of neuron cells. Each of these cells is like a tiny computer with extremely limited capabilities; however, connected together, these cells form the most intelligent system known. Neural networks are formed from hundreds or thousands of simulated neurons connected together in much the same way as the brain's neurons.
Just like people, neural networks learn from experience, not from programming. Neural networks are good at pattern recognition, generalization, and trend prediction. They are fast, tolerant of imperfect data, and do not need formulas or rules. Neural networks are trained by repeatedly presenting examples to the network. Each example includes both inputs (information you would use to make a decision) and outputs (the resulting decision, prediction, or response).
Your network tries to learn each of your examples in turn, calculating its output based on the inputs you provided. If the network output doesn't match the target output, BrainMaker corrects the network by changing its internal connections. This trial-and-error process continues until the network reaches your specified level of accuracy. Once the network is trained and tested, you can give it new input information, and it will produce a prediction. Designing your neural network is largely a matter of identifying which data is input, and what you want to predict, assess, classify, or recognize.
If your problem can be predicted from history, probably yes.
BrainMaker lets you develop smart applications even if you have no special computer skills. BrainMaker learns to recognize patterns and make predictions using examples you have collected or created. The training process is similar to using a deck of flash cards - your neural network learns to associate certain conditions with particular results. After BrainMaker trains and tests the network, you can apply what it has learned to new situations.
To solve a specific problem, you collect examples of what you want your computer to recognize, forecast, or predict. Using BrainMaker's built-in utility, NetMaker, you can import data from:
In addition, BrainMaker Professional reads the following file formats:
NetMaker works like a spreadsheet to organize training examples for your network, performing calculations such as moving averages and differences. Then BrainMaker automatically creates and trains your network from the examples you've provided. BrainMaker reads all training and testing data from your hard disk, so there is no limit to the amount of data you can use.
If you'd like to read more about neural networks, check out our reading reference list or order our book Introduction to Neural Networks.
Next, read about Designing Neural Networks with BrainMaker.