On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Training Finnish and English Speakers to Produce Non-native Vowels

The subject of this thesis was the acquisition of difficult non-native vowels by speakers of two different languages. In order to study the subject, a group of Finnish speakers and another group of American English speakers were recruited and they underwent a short listen-and-repeat training that included as stimuli the semisynthetically created pseudowords /ty:ti/ and /tʉ:ti/. The aim was to study the effect of the training method on the subjects as well as the possible influence of the speakers’ native language on the process of acquisition. The selection of the target vowels /y/ and /ʉ/ was made according to the Speech Learning Model and Perceptual Assimilation Model, both of which predict that second language speech sounds that share similar features with sounds of a person’s native language are most difficult for the person to learn. The vowel /ʉ/ is similar to Finnish vowels as well as to vowels of English, whereas /y/ exists in Finnish but not in English, although it is similar to other English vowels. Therefore, it can be hypothesized that /ʉ/ is a difficult vowel for both groups to learn and /y/ is difficult for English speakers. The effect of training was tested with a pretest-training-posttest protocol in which the stimuli were played alternately and the subjects’ task was to repeat the heard stimuli. The training method was thought to improve the production of non-native sounds by engaging different feedback mechanisms, such as auditory and somatosensory. These, according to Template Theory, modify the production of speech by altering the motor commands from the internal speech system or the feedforward signal which translates the motoric commands into articulatory movements. The subjects’ productions during the test phases were recorded and an acoustic analysis was performed in which the formant values of the target vowels were extracted. Statistical analyses showed a statistically significant difference between groups in the first formant, signaling a possible effect of native motor commands. Furthermore, a statistically significant difference between groups was observed in the standard deviation of the formants in the production of /y/, showing the uniformity of native production. The training had no observable effect, possibly due to the short nature of the training protocol.