- Local Weather Predictor
Input parameters such as temperature, pressure, humidity, wind-speed, wind direction, relative day length, proportion of cloud cover, daylight intensity, solar angle, etc to a multi-layer perceptron. Perhaps also enter the first and even higher-order differentials of these parameters as additional inputs. Produce an output giving a useful classification of the weather at prescribed times into the future. The classifications could vary according to purpose, eg human comfort, farming, building.
- Harvesting Predictor
Inputs to a neural network from the local weather predictor (see above) combined with inputs from a spectrum analyser of the light being reflected from a crop could provide an output giving advanced advice of ideal instant to harvest a crop.
- HF Radio Conditions Predictor
Radio path conditions on the HF band (7 - 30 MHz) vary in a way similar in nature to (but not necessarily significantly related to) the weather. Input such things as the frequency and strength of VLF whistlers, current signal conditions, time of day, season, phase of the 11/22-year sun spot cycle, to a neural network. With training, the output could be made to predict the communication conditions at various prescribed lengths of time into the future for given directions, paths or destinations. This could even distinguish between divergent conditions on different bands, giving a probability conditions of good or bad contact.
- Radio Signal Identification
A neural network could be trained to recognise its owner's call sign when someone else is calling its owner. It could also be made to recognise who is calling. This could fulfil for voice radio the role of the bell on a telephone to alert the person being called.
- Relaxing Mobile Networks
The travelling salesman problem of finding the shortest route between a set of towns. The particular application of current interest is that of finding the shortest links in a communications network whose nodes are mobile. The conjecture is that this is best solved by algorithm rather than by minimising the energy function of an elastic network or by running a Hopfield network. However, these networks may be better at solving other constraint satisfaction problems such as best crop mix in view of climatic and market conditions.
- Personal Identification
A neural network could be trained to recognise a person's face, signature, finger print, DNA or iris patterns all together to give a strong reinforcement to recognition.
- Personal Compatibility Prediction
Given certain facts about a person, a neural network could be taught to predict whether or not the person would be a good client, business associate, friend, spouse, employer, employee, advisor, confidante, etc. for you - a given individual. It could also be taught which type of companies would be good clients or suppliers for you specifically. The network would have to be taught using good and bad past examples. It could also be used to assess which occupation or mode of working would be most beneficial or acceptable to a given person. It could also have uses in political analysis and psychology.
- Maintaining Dynamic Trim
An aircraft (or ship) in rough weather could be kept in trim and on course by a neural network acting as the flight control and navigation computer. Inputs such as speed, rate of climb/descent, rate of turn, thrust, pitch, roll, height error, course error could be provided. It could then be trained to give appropriate output of pitch, roll, flaps, thrust and rudder commands. The network could be taught by recording the conditions on a flight and the actions of an experienced pilot.
- Predicting Market Trends
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