TAILIEUCHUNG - Innovations in Robot Mobility and Control - Srikanta Patnaik et al (Eds) Part 10

Tham khảo tài liệu 'innovations in robot mobility and control - srikanta patnaik et al (eds) part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 5 Intelligent Neurofuzzy Control of Robotic Gripper 171 Fig. Block diagram of the hybrid supervised reinforcement system in which a Supervised Learning Network SLN trained on pre-labelled data is added to the basic GARIC architecture Hybrid Learning Looking to have a faster adaptation to environmental changes we have implemented a hybrid learning approach which uses both supervised and reinforcement learning. The combination of these two training algorithms allows the system to have a faster adaptation 16 . The hybrid approach has not only the characteristic of self-adaptation but the ability to make best use of knowledge . pre-labelled training data should they exist. The proposed hybrid algorithm is also based on the GARIC architecture. An extra neurofuzzy block the supervised learning network SLN is added to the original structure Figure . The SLN is a neurofuzzy controller which is trained in non-real time with supervised back-propagation. When new training data are available the SLN is retrained without stopping the system execution then it sends a parameter updating 172 . Domínguez-López et al. signal to the action selection network. The ASN parameters can now be updated if appropriate. As new training data become available during system operation see below the SLN loads the rule-weight vector from the ASN and starts its re training which continues until the stop criterion is reached average error less than or equal to V2 see Section . The information loaded rule confidence vector from the ASN is utilised as a priori knowledge by the SLN. Once the SLN training has finished the new rule weight vector is sent back to the ASN. Elements of the confidence vector . weights are transferred from the SLN to the ASN only if the difference between them is lower than or equal to 5 if wfN A wfN wfLN 3 thenwẠSN wfLN V i where counts over all corresponding ASN and SLN weights. Neurofuzzy techniques do not require a .

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