NeuNet Pro 2.3 for Windows
The following is an imaginative list of possible uses:
Air traffic control
could be automated with the location, altitude, direction and speed of each radar blip as input to the network. The output would be the air traffic controller's instruction to each blip.
, predator/prey relationships, and population cycles may be suitable for analysis by neural networks.
is a way to uncover certain data records which do not fit the pattern of their peers.
Appraisal and valuation
of property, buildings, automobiles, machinery, etc. should be an easy task for a neural network.
on horse races, stock markets, sporting events, etc. could be based on neural network predictions.
could be predicted using a large sample of crime details as input and the resulting sentences as output.
Complex physical and chemical processes
that may involve the interaction of numerous (possibly unknown) mathematical formulas can be modeled heuristically using a neural network.
Data mining, cleaning & validation
can be achieved by determining which records suspiciously diverge from the pattern of their peers.
Direct mail advertisers
could use neural network analysis of their database to decide which customers should be targeted, and avoid wasting money on unlikely targets.
from sonar, radar, seismic and magnetic instruments can be used to predict their targets.
based on neural networks should be more realistic than older models based on classical statistics.
could be optimized if the neural network were able to predict which job applicant will achieve the best job performance.
could package their intuitive expertise into a neural network, to automate their services.
could predict their demand for electricity. Then load shedding could be done to reduce the electric bill, and reduce impact on the power company.
regarding credit cards, insurance or taxes could be automated using a neural network analysis of past incidents.
Handwriting and typewriting
can be recognized by imposing a grid over the writing, then each square of the grid becomes an input to the neural network. This is called "Optical Character Recognition"
Lake water levels
could be predicted based on precipitation patterns and River/Dam flows.
could be automated by capturing the actions of experienced machine operators into a neural network.
is an ideal application for neural networks.
relies heavily on classical statistics to analyze research data. Perhaps a neural network should be included in the researcher's toolkit.
has been tried using neural networks. The network is trained to recognize patterns in the pitch and tempo of certain music, then the network writes its own music.
Photos & fingerprints
could be recognized by imposing a fine grid over the photo. Each square of the grid becomes an input to the network.
Recipes and chemical formulations
could be optimized based on the predicted outcome of a formula change.
could be optimized by predicting demand based on past patterns.
River water levels
could be predicted based on upstream reports, and the time and location of each report.
Scheduling of buses, airplanes, and elevators"
could be optimized by predicting demand.
requirements for restaurants, retail stores, police stations, banks, etc. could be predicted based on day of week, pay-days, holidays, weather, season, etc.
for games, business and war can be captured by analyzing the expert player's response to given stimuli. For example, a football coach must decide whether to kick, pass, or run on the last down. The inputs for this decision include score, time, field location, yards to first down, etc.
could be predicted, so that signal timing could be optimized. The network could recognize "a weekday morning rush hour during a school holiday" or "a typical winter Sunday morning."
can be obtained by analyzing the audio oscilloscope pattern, much like a stock market graph.
may be possible. Inputs would include weather reports from surrounding areas. Output(s) would be the future weather in our area. Effects like ocean currents and jet stream could be included.
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