Synthesis of formal and fuzzy logic to detect patterns in clutter

Recognizing patterns in data often relies on rules, or exploits simple features in the data. However, when noise or clutter obscures these features in the data, one must consider a number of different features to determine the best match. This often leads to combinatorial complexity manifested in either of two ways, complexity of learning or complexity of computations. Adaptive model-based approaches potentially offer better computational performance than feature-based methods and may lead to extracting the maximum information from data. These techniques still often relied on using formal logic to compare library models to incoming data. Neural networks are usually not easy for implementing model-based approaches. Fuzzy logic bypasses using formal logic, but it provides solutions that often are heavily influenced by the initial degree of fuzziness. We are developing a technique for detecting patterns below clutter based on the neural network modeling field theory. Modeling field theory (MFT) using fuzzy dynamic logic to overcome combinatorial complexity is introduced along with an algorithm suitable for the detection of patterns below clutter. This new mathematical technique is inspired by the analysis of biological systems, like the human brain, which combines conceptual understanding with emotional evaluation and overcomes the combinatorial complexity of model-based techniques.