Name
Abstract | ||
|---|---|---|
Artificial neural networks are a dominant force in our modern era of data-driven artificial intelligence. The adaptive neuro fuzzy inference system (ANFIS) is a neural network based on fuzzy logic versus a more traditional premise like convolution. Advantages of ANFIS include the ability to encode and potentially understand machine learned neural information in the pursuit of explainable, interpretable, and ultimately trustworthy artificial intelligence. However, real-world data is almost always imperfect, e.g., incomplete or noisy, and ANFIS is not naturally robust. Specifically, ANFIS is susceptible to over inflated uncertainty, poor antecedent (fuzzy set) data alignment, degenerate optimization conditions, and hard to interpret logic, to name a few factors. Herein, we explore the use of possibilistic clustering to identify outliers, specifically typicality degrees, to increase the robustness of ANFIS; or any fuzzy logic neuron/network. Experiments are presented that demonstrate the need and quality of the proposed solutions in the pursuit of robust interpretable machine learned neuro fuzzy logic solutions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/FUZZ48607.2020.9177593 | FUZZ-IEEE |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Blake Ruprecht | 1 | 0 | 0.34 |
| Wenlong Wu | 2 | 0 | 0.34 |
| muhammad aminul islam | 3 | 14 | 5.66 |
| Derek T. Anderson | 4 | 150 | 25.17 |
| James M. Keller | 5 | 3201 | 436.69 |
| Grant J. Scott | 6 | 214 | 22.19 |
| Curt H. Davis | 7 | 430 | 51.00 |
| Frederick E. Petry | 8 | 562 | 69.24 |
| Paul Elmore | 9 | 20 | 4.71 |
| Kristen Nock | 10 | 0 | 0.34 |
| Elizabeth Gilmour | 11 | 0 | 0.34 |