Prof. Dr. Javier Echanobe: Unterschied zwischen den Versionen
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| − | |Titel DE= | + | |Titel DE=PWM-ANFIS. A Computational Efficient Neuro-Fuzzy System |
| − | |Titel EN= | + | |Titel EN=PWM-ANFIS. A Computational Efficient Neuro-Fuzzy System |
| − | |Beschreibung DE= | + | |Beschreibung DE=Fuzzy Systems (FS) and Neural Networks (NN) are widely used techniques in Intelligent Systems. These systems cover many different application areas such as automatic control, pattern recognition, human-machine interaction, expert systems, modelling, medical diagnosis, economics, etc. Both techniques have their own advantages and drawbacks. FS have the ability to represent comprehensive linguistic knowledge and perform reasoning by means of rules. However, FS do not provide a mechanism to automatically acquire and/or tune those rules. On the other hand NN are adaptive systems that can be trained and tuned from a set of samples. Nevertheless, it is very difficult to extract and understand the acquired knowledge. In other words, FS and NN are complementary paradigms. |
| − | |Beschreibung EN= | + | |
| + | Neuro-Fuzzy (NF) systems have been proposed to combine the advantages of both techniques, as well as overcome the drawbacks of each one individually. These systems can combine both fuzzy and neuro paradigms in two different ways: (a) by introducing the fuzzification into the neural-network structure and (b) by providing the FS with learning ability by means of NN algorithms. | ||
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| + | However, NF systems are rather complex because they integrate many different tasks working in a cooperative way. To overcome this drawback we propose a NF system in which the complexity is highly reduced without sacrificing appreciably its features or capabilites. The system is of the same type as the well-known ‘‘adaptive-network-based fuzzy inference system” (ANFIS) method about which many related works have been written. However, some different restrictions are applied to the system in order to reduce considerably the complexity of its inference mechanism. We call this system PWM-ANFIS as it provides a Piecewise Multilinear output. | ||
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| + | |Beschreibung EN=Fuzzy Systems (FS) and Neural Networks (NN) are widely used techniques in Intelligent Systems. These systems cover many different application areas such as automatic control, pattern recognition, human-machine interaction, expert systems, modelling, medical diagnosis, economics, etc. Both techniques have their own advantages and drawbacks. FS have the ability to represent comprehensive linguistic knowledge and perform reasoning by means of rules. However, FS do not provide a mechanism to automatically acquire and/or tune those rules. On the other hand NN are adaptive systems that can be trained and tuned from a set of samples. Nevertheless, it is very difficult to extract and understand the acquired knowledge. In other words, FS and NN are complementary paradigms. | ||
| + | |||
| + | Neuro-Fuzzy (NF) systems have been proposed to combine the advantages of both techniques, as well as overcome the drawbacks of each one individually. These systems can combine both fuzzy and neuro paradigms in two different ways: (a) by introducing the fuzzification into the neural-network structure and (b) by providing the FS with learning ability by means of NN algorithms. | ||
| + | |||
| + | However, NF systems are rather complex because they integrate many different tasks working in a cooperative way. To overcome this drawback we propose a NF system in which the complexity is highly reduced without sacrificing appreciably its features or capabilites. The system is of the same type as the well-known ‘‘adaptive-network-based fuzzy inference system” (ANFIS) method about which many related works have been written. However, some different restrictions are applied to the system in order to reduce considerably the complexity of its inference mechanism. We call this system PWM-ANFIS as it provides a Piecewise Multilinear output. | ||
| + | |||
| + | |Veranstaltungsart=Kolloquium Angewandte Informatik | ||
|Start=2015/05/08 14:00:00 | |Start=2015/05/08 14:00:00 | ||
|Ende=2015/05/08 15:00:00 | |Ende=2015/05/08 15:00:00 | ||
|Gebäude=11.40 | |Gebäude=11.40 | ||
|Raum=231 | |Raum=231 | ||
| + | |Vortragender=Prof. Dr. Javier Echanobe | ||
|Eingeladen durch=Hartmut Schmeck | |Eingeladen durch=Hartmut Schmeck | ||
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
|In News anzeigen=True | |In News anzeigen=True | ||
}} | }} | ||
Version vom 14. April 2015, 11:39 Uhr
PWM-ANFIS. A Computational Efficient Neuro-Fuzzy System
Veranstaltungsart:
Kolloquium Angewandte Informatik
Fuzzy Systems (FS) and Neural Networks (NN) are widely used techniques in Intelligent Systems. These systems cover many different application areas such as automatic control, pattern recognition, human-machine interaction, expert systems, modelling, medical diagnosis, economics, etc. Both techniques have their own advantages and drawbacks. FS have the ability to represent comprehensive linguistic knowledge and perform reasoning by means of rules. However, FS do not provide a mechanism to automatically acquire and/or tune those rules. On the other hand NN are adaptive systems that can be trained and tuned from a set of samples. Nevertheless, it is very difficult to extract and understand the acquired knowledge. In other words, FS and NN are complementary paradigms.
Neuro-Fuzzy (NF) systems have been proposed to combine the advantages of both techniques, as well as overcome the drawbacks of each one individually. These systems can combine both fuzzy and neuro paradigms in two different ways: (a) by introducing the fuzzification into the neural-network structure and (b) by providing the FS with learning ability by means of NN algorithms.
However, NF systems are rather complex because they integrate many different tasks working in a cooperative way. To overcome this drawback we propose a NF system in which the complexity is highly reduced without sacrificing appreciably its features or capabilites. The system is of the same type as the well-known ‘‘adaptive-network-based fuzzy inference system” (ANFIS) method about which many related works have been written. However, some different restrictions are applied to the system in order to reduce considerably the complexity of its inference mechanism. We call this system PWM-ANFIS as it provides a Piecewise Multilinear output.
(Prof. Dr. Javier Echanobe)
Start: 08. Mai 2015 um 14:00
Ende: 08. Mai 2015 um 15:00
Im Gebäude 11.40, Raum: 231
Veranstaltung vormerken: (iCal)
Veranstalter: Forschungsgruppe(n) Effiziente Algorithmen