Fuzzy logic and neural networks are methods whose time has yet to come. Fuzzy type logic is already used in things like speech recognition software, but neural networks havn't seen much commercial success.

The main problems with neural networks is that most of the algorithms developed so far require extensive training periods based upon hand-picked examples. Where neural networks do make a difference is in things like face recognition, recognising different types of vehicle travelling along a road, or really any situation involving visual identification. Although vision systems have been used extensively in industry they havn't yet broken out into the mainstream consumer market. As digital cameras and processing power becomes ever cheaper I think that situation will change over the next 5 years, and we will start to see things like childrens toys with face and voice recognition technology.

I agree with Motters that ANNs and Fuzzy are viable but still need a little more proving. The only problem is that it seems that there are very few people who actually understand the concepts and are able to communicate them to other people. I am very interested in these things but was not convinced of my lecturers understanding of these ares when they tried to explain the workings to our group. I do believe these have futures, but may need a little more testing first.

I agree on neural nets. I think they have a lot of potential that has yet to be recognized and developed. I still see a lot of activity involving neural nets on both the research side and in practical applications. They're still in their infancy. Fuzzy logic, on the other hand, has turned out to be something of a bust. It's not that it didn't work. But it's just a fancy name for one particular method of doing something that engineers had been doing for years anyway. Bob Pease wrote a series of pretty damning articles in which he takes fuzzy logic to task and challenges its claimed virtues, showing that traditional engineering methods beat it in every case where he could get enough verifiable data to build a conventional controller. (which wasn't many as most of the well-known uses of fuzzy logic hyped by proponents turned out to be bogus or highly exagerated).

I was going to link them to the above article but could only find three of the five online. Here are the ones that I could find:

What's all this Fuzzy Logic Stuff, Anyhow? Part I - Anyone have a link?? In this one he first stated his problems with fuzzy logic hype and challenged proponents to provide any evidence - in a form he could reproduce in the lab, such as schematics or code - that fuzzy logic could do anything better than conventional techniques like PID.

What's all this Fuzzy Logic Stuff, Anyhow? Part II

What's all this Fuzzy Logic Stuff, Anyhow? Part III (link?)

What's all this Fuzzy Logic Stuff, Anyhow? Part IV

What's all this Fuzzy Logic Stuff, Anyhow? Part V

More thoughts on Fuzzy logic

And one other related article:

Some Crisp Thoughts on Fuzzy Logic by Daniel Abramovitch of HP Labs.

With binary logic you can emulate fuzzy logic and neural networks, which makes them a subset of the full capabilities of binary. From what I can see the only advantages of having true fuzzy/neural circuits is to save space/time in your design, and I'm not sure those advantages are worth it for any design.

Fuzzy Logic, within the realm of control electronics and process control, is actually an established and flourishing technology, especially in Japan. Outside of the that dual niche it gets a lot more spotty.

Neural nets are also a niche dominant technology. Credit card fraud systems have been in use for many years using neural net technology, and many popular OCR systems use them.

It's more of a matter of finding a conducive problem space with Fuzzy Logic, one that fits the solution. With neural nets, the other posters comments about the difficulty in using them come into play. You need a high skill level in many different fields, especially statistics and data mining, to really make them fly.

A friend and I were talking the other day about how to design a control algorithm that could fly a small autonomous plane through a forest. Basiaclly, we figured, a dragonfly can do a great job. Yet a dragonfly has hardly any brain at all.

Clearly the dragonfly's ability to navigate a forest is not based on complex algorithms, but some pretty tight wiring between eyes and wings. "Something close to the left, shift right!" etc.

Neural nets and fuzzy sets seem a much better way to imagine how you'd implement this.

Yep, I'm always fascinated by dragonflies and insect-level intelligence in general. I've seen several optical flow implementations that use neural networks. Optical flow is essentially what dragflies (and other insects with compound eyes) are using. The output of a well-designed optical flow vision system provides vector based information very similar to what you'd get from the gyro/accelerometer combination in an IMU. It can tell you yaw, roll, and pitch accelerations and velocities as well as heading information; all without traditional, complex vision processing algorithms. It's subject to certain types of optical illusions in much the same way human vision is but it seems to work pretty well for dragonflies...

I disagree that "there are very few people who actually understand the concepts". Maybe they're not being communicated to a wide audience very well but the concepts behind Neural Nets are very well understood by many people.

And I strongly agree with some of the negative comments about Fuzzy Logic. While I'm not a fan of Neural Nets I think they do have a place in many people's kits of parts. On the other hand Fuzzy Logic is a completely trivial subject. It is somethng that has been reinvented many times by many people because it is so simple and many of those people move onto more sophisticated methods when they eventually learn them.