Intelligent Adaptive Systems for Real World Applications
Intelligent adaptive systems learn from data to improve their performance on a task as they gain experience on the task. They may incorporate machine learning and pattern recognition techniques such as neural networks.
Data
Machine learning and pattern recognition systems need data:
If your organisation already has data, consider what information could be revealed by the data or what application the data could be used for?
If your organisation does not have data then consider if perhaps it is already generated dynamically by some process within your organisation or otherwise could either be collected or generated for future use?
Data could come from multiple sources and be fused to provide solutions and applications not otherwise thought of.
For best results the data should be a balanced representation of the application domain.
Identify Applications
One approach to identifying applications in your organisation is to ask if there are any existing systems with poor performance that could be improved by machine learning and pattern recognition techniques?
The following benefits and examples may help:
Benefits
Reduce cost
Improve optimisation
Improve accuracy
Increase efficiency
Optimise material use
Reduce waste
Generalisation
Applications
Optimisation
Categorisation
Classification
Prediction
Forecasting
Robotics
Automation
Control
Systems modelling
Machine health monitoring
Recognising patterns
Recognising anomalies
Fault diagnosis
Quality control
Recommender systems
Application Areas
Big Data
Energy
Aerospace
Environmental
Medical
Pharmaceutical
Retail
Scientific
Engineering
Manufacturing
Government
Utilities
Agricultural
What Next?
Contact KWiSystems explaining what data you have or could collect or generate and what results or information you would like to obtain from the data.
