Neuroemulators

The availability and growing computing capabilities of modern computers have led to a wide distribution of programs using the principles of neural network data processing, but executed on serial computers. This approach does not take advantage of the parallelism inherent in neuroscience, it focuses solely on the ability of neural networks to solve formalized tasks.

The advantages of such "virtual" neurocomputers for relatively small tasks are obvious:

• No need to spend money on new equipment, if you can use general-purpose computers.
• The user should not master the features of programming on special processors and how to connect them to the base computer.
• Mainframe computers do not impose any restrictions on the structure of networks and methods of their training, while special processors most often have a limited set of “wired” activation functions and reach peak performance on a certain range of tasks.

Neuroprogramming software can be divided into ready-made general purpose neuro package, more expensive neurozastosuvan development systems, which have more features, but require more knowledge, and, finally, ready-made integrated solutions with elements of neural network information processing that are usually hidden from the user's eyes.

ready neuropackage

These are complete independent software products intended for a wide class of tasks, mainly for predictions and statistical data processing. Most neuropackages have a friendly interface that does not require familiarity with programming languages.

Free products are usually a multi-layer perceptron with one or more learning rules. The exception is quite professional SNNS (Stuttgart Neural Network Simulator) with a large set of features, it works, however, only on UNIX-based machines.

Commercial packages differ from those freely distributed by a large set of import and data preprocessing tools, with additional capabilities for analyzing the significance of inputs and optimizing the network structure. The cost of commercial emulators is $ 1000.

As a rule, such packages (BrainMaker Professional, NeuroForecaster, Lora-IQ300) have their own built-in preprocessing unit, although sometimes it is more convenient to use standard spreadsheets for this purpose.

Such packages are aimed at solving information problems in an interactive mode - with the direct participation of the user. They are not suitable for use in streamline data processing. In addition, they are not adapted for developing complex data processing systems consisting of many blocks containing, for example, hundreds of neural networks, adaptively tuned and updated on new data. The development of such "serious" systems requires special tools.

Neurosastosuvan Development Tools

The main thing that distinguishes this class of software is the ability to generate software code that uses trained neural networks to process data. Such code can be embedded as a subsystem in any complex information systems.

Examples of such systems are NeuralWorks Professional II Plus (priced from $ 3000) by NeuralWare and the Russian Bench (neuro-workbench). The latter can generate codes in many languages, including Java. Such Java applets can be used to organize various kinds of services in global and local networks.

A convenient tool for the development of complex neural systems is MATLAB with neural network tools attached to it, which seamlessly fits into the matrix ideology of this system. MATLAB provides a convenient environment for the synthesis of neural network methods with other data processing methods (wavelet analysis, statistics, financial analysis, etc. ). Applications developed in the MATLAB system can then be retransmitted to C +.

Such development tools are used by firms to create ready-made solutions in various fields, based on neural network data processing.

Ready solutions based on neural networks

This is the end result. Here, neural networks are hidden from the user in the depths of ready-made automated systems designed to solve specific production problems. For example, the Falcon product is embedded in an automated banking system for servicing payments using plastic cards. Otherwise, it will be an automated plant or reactor management system.

The end user, as a rule, is not interested in the way to achieve the result, only the quality of the product is important to him. Since many such ready-made solutions have unique capabilities and provide real competitive advantages, their price can be quite high - much higher than the cost of neuro-hardware.

neural network consulting

Instead of selling ready-made programs or tools for their development, you can trade in services. Some tasks, such as forecasting market time series, are so complex, available only for real professionals. Not every company can afford the costs associated with advanced scientific developments (for example, constant participation in international conferences). Therefore, firms are gaining popularity, the only products of which is the prediction of markets. With a large number of clients, the price of such predictions can be quite moderate.

neuro emulator FuzzySearch

http://www.basegroup.ru/download/demoprg/hamming/

The program is designed to demonstrate the capabilities of Hamming networks in pattern recognition.

The task of fuzzy search favorites as a simple and clear example. This is far from the only application of these networks. For example, they are used to restore images with incomplete or distorted information.

The Hamming network is a type of neural network. The principle of operation of Hamming nets is based on determining the Hamming distance between objects and finding the closest one.

Associative memory - the use of Hamming networks for fuzzy search

The Hamming distance is the number of distinct bits in two binary vectors. To encode letters into numbers, this application uses the ASCII code, although other encoding methods can be used.

If you successfully select the encoding, you can significantly improve the quality of recognition. For example, to correct typos should be taken into account the location of the letters on the keyboard. Encoding should be designed in such a way that the letters that are located next to the keyboard have similar (according to Heming) codes.

For the system to work, you must have a file with images (dictionary). To do this, open any text file. Based on this file, the system itself will compile a dictionary. After that, you need to enter a word to search for, the program will find the word closest to it and fix a pointer on it.

Neural Emulator Neural Network Wizard

http://www.basegroup.ru/download/demoprg/nnw/

Neural Network Wizard 1.7 is a neurocomputer software emulator. The Neural Network Wizard implements a multilayer neural network that learns using the back propagation error algorithm. The program can be used to analyze information, build models of processes and forecasting.

To work with the system it is necessary to carry out the following operations:

• Collect process statistics.
• Teach a neural network on the data.
• Check the results.

During study, the neural network independently selects the values ​​of the coefficients and builds such a model, most accurately reflects the research process.

Emulator features:

• Getting data for training from a text file
• Various methods of data rationing.
• Creation of multilayer neural networks of various configurations.
• Setting the neuro system learning parameters
• Ability to save learning outcomes
• Automation learning system.
• Automatic formation teaches and test sets.
• Open source code (version 1.7).

Neuroemulator Sharky Neural Network

http://www.sharktime.com/

Sharky Neural Network is a free computer program that performs classification using neural networks. The Sharky Neural Network program is designed for educational purposes to better understand neural networks.

Sharky Neural Network classifies 2D points into two different classes (yellow and blue). It does not classify forms, forms can be seen as the visualization of arrays of classified points. The program recognizes only blue and yellow 2D-dots, described by two values.